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NIRCam PSF Photometry Notebook

Data: NIRCam simulated images obtained using MIRAGE and run through the JWST pipeline of the Large Magellanic Cloud (LMC) Astrometric Calibration Field. Simulations is obtained using a 4-pt subpixel dither for three couples of wide filters: F070W, F115W, and F200W for the SW channel, and F277W, F356W, and F444W for the LW channel. We simulated only 1 NIRCam SW detector (i.e., "NRCB1").

For this example, we use Level-2 images (.cal, calibrated but not rectified) for two SW filters (i.e., F115W and F200W) and derive the photometry in each one of them. The images for the other filters are also available and can be used to test the notebook and/or different filters combination.

The notebook is divided in two parts: in Part I we show how to create a PSF model and perform the PSF photometry, whereas in Part II, we show how to derive the final calibrated Color-Magnitude Diagram.

PSF Photometry can be obtained using:

  • single model obtained from WebbPSF
  • grid of PSF models from WebbPSF
  • single effective PSF (ePSF)

Work in Progress:

  • create a grid of ePSF and perform reduction using the ePSF grid
  • use the ePSF grid to perturbate the WebbPSF model

The notebook shows:

  • how to obtain the PSF model from WebbPSF (or build an ePSF)
  • how to perform PSF photometry on the image
  • how to cross-match the catalogs of the different images
  • how to derive and apply photometric zeropoint

Final plots show:

  • Instrumental Color-Magnitude Diagrams for the 4 images
  • Instrumental Color-Magnitude Diagrams and errors
  • Magnitudes Zeropoints
  • Calibrated Color-Magnitude Diagram (compared with Input Color-Magnitude Diagram)
  • Comparison between input and output photometry

Note on pysynphot: Data files for pysynphot are distributed separately by Calibration Reference Data System. They are expected to follow a certain directory structure under the root directory, identified by the PYSYN_CDBS environment variable that must be set prior to using this package. In the example below, the root directory is arbitrarily named /my/local/dir/trds/. \ export PYSYN_CDBS=/my/local/dir/trds/ \ See documentation here for the configuration and download of the data files.

Import Functions

In [1]:
import os
import sys
import time

import numpy as np

import pandas as pd

import glob as glob

import jwst
from jwst.datamodels import ImageModel

import tarfile

import urllib.request

from astropy import wcs
from astropy import units as u
from astropy.io import fits
from astropy.visualization import (ZScaleInterval, SqrtStretch, ImageNormalize)
from astropy.visualization import simple_norm
from astropy.nddata import Cutout2D, NDData
from astropy.stats import gaussian_sigma_to_fwhm
from astropy.table import Table, QTable
from astropy.modeling.fitting import LevMarLSQFitter
from astropy.wcs.utils import pixel_to_skycoord
from astropy.coordinates import SkyCoord, match_coordinates_sky
from astropy.stats import sigma_clipped_stats

from photutils import CircularAperture, EPSFBuilder, find_peaks, CircularAnnulus
from photutils.detection import DAOStarFinder, IRAFStarFinder
from photutils.psf import DAOGroup, IntegratedGaussianPRF, extract_stars, IterativelySubtractedPSFPhotometry
from photutils.background import MMMBackground, MADStdBackgroundRMS
from photutils.centroids import centroid_2dg
from photutils import aperture_photometry

from ipywidgets import interact

import webbpsf
from webbpsf.utils import to_griddedpsfmodel

import pysynphot  # PYSIN_CDBS must be defined in the user's environment (see note above)
/tmp/nbcollection-ci/scanner-build-dir/jdat_notebooks/psf_photometry/lib/python3.8/site-packages/pysynphot/locations.py:345: UserWarning: Extinction files not found in /tmp/nbcollection-ci/scanner-build-dir/jdat_notebooks/psf_photometry/grp/redcat/trds/extinction
  warnings.warn('Extinction files not found in %s' % (extdir, ))
/tmp/nbcollection-ci/scanner-build-dir/jdat_notebooks/psf_photometry/lib/python3.8/site-packages/pysynphot/refs.py:117: UserWarning: No graph or component tables found; functionality will be SEVERELY crippled. No files found for /tmp/nbcollection-ci/scanner-build-dir/jdat_notebooks/psf_photometry/grp/redcat/trds/mtab/*_tmg.fits
  warnings.warn('No graph or component tables found; '
/tmp/nbcollection-ci/scanner-build-dir/jdat_notebooks/psf_photometry/lib/python3.8/site-packages/pysynphot/refs.py:124: UserWarning: No thermal tables found, no thermal calculations can be performed. No files found for /tmp/nbcollection-ci/scanner-build-dir/jdat_notebooks/psf_photometry/grp/redcat/trds/mtab/*_tmt.fits
  warnings.warn('No thermal tables found, '
**WARNING**: LOCAL JWST PRD VERSION PRDOPSSOC-034 DOESN'T MATCH THE CURRENT ONLINE VERSION PRDOPSSOC-036
Please consider updating pysiaf, e.g. pip install --upgrade pysiaf or conda update pysiaf

Import Plotting Functions

In [2]:
%matplotlib inline
from matplotlib import style, pyplot as plt
import matplotlib.patches as patches
import matplotlib.ticker as ticker

plt.rcParams['image.cmap'] = 'viridis'
plt.rcParams['image.origin'] = 'lower'
plt.rcParams['axes.titlesize'] = plt.rcParams['axes.labelsize'] = 30
plt.rcParams['xtick.labelsize'] = plt.rcParams['ytick.labelsize'] = 30

font1 = {'family': 'helvetica', 'color': 'black', 'weight': 'normal', 'size': '12'}
font2 = {'family': 'helvetica', 'color': 'black', 'weight': 'normal', 'size': '20'}

Load the images and create some useful dictionaries

We load all the images and we create a dictionary that contains all of them, divided by detectors and filters. This is useful to check which detectors and filters are available and to decide if we want to perform the photometry on all of them or only on a subset (for example, only on the SW filters).

We also create a dictionary with some useful parameters for the analysis. The dictionary contains the photometric zeropoints (from MIRAGE configuration files) and the NIRCam point spread function (PSF) FWHM, from the NIRCam Point Spread Function JDox page. The FWHM are calculated from the analysis of the expected NIRCam PSFs simulated with WebbPSF.

Note: this dictionary will be updated once the values for zeropoints and FWHM will be available for each detectors after commissioning.

Hence, we have two dictionaries:

  • dictionary for the single Level-2 calibrated images
  • dictionary with some other useful parameters
In [3]:
dict_images = {'NRCA1': {}, 'NRCA2': {}, 'NRCA3': {}, 'NRCA4': {}, 'NRCA5': {},
               'NRCB1': {}, 'NRCB2': {}, 'NRCB3': {}, 'NRCB4': {}, 'NRCB5': {}}

dict_filter_short = {}
dict_filter_long = {}

ff_short = []
det_short = []
det_long = []
ff_long = []
detlist_short = []
detlist_long = []
filtlist_short = []
filtlist_long = []

if not glob.glob('./*cal*fits'):

    print("Downloading images")

    boxlink_images_lev2 = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/stellar_photometry/images_level2.tar.gz'
    boxfile_images_lev2 = './images_level2.tar.gz'
    urllib.request.urlretrieve(boxlink_images_lev2, boxfile_images_lev2)

    tar = tarfile.open(boxfile_images_lev2, 'r')
    tar.extractall()

    images_dir = './'
    images = sorted(glob.glob(os.path.join(images_dir, "*cal.fits")))

else:

    images_dir = './'
    images = sorted(glob.glob(os.path.join(images_dir, "*cal.fits")))

for image in images:

    im = fits.open(image)
    f = im[0].header['FILTER']
    d = im[0].header['DETECTOR']

    if d == 'NRCBLONG':
        d = 'NRCB5'
    elif d == 'NRCALONG':
        d = 'NRCA5'
    else:
        d = d

    wv = np.float(f[1:3])

    if wv > 24:         
        ff_long.append(f)
        det_long.append(d)

    else:
        ff_short.append(f)
        det_short.append(d)   

    detlist_short = sorted(list(dict.fromkeys(det_short)))
    detlist_long = sorted(list(dict.fromkeys(det_long)))

    unique_list_filters_short = []
    unique_list_filters_long = []

    for x in ff_short:

        if x not in unique_list_filters_short:

            dict_filter_short.setdefault(x, {})

    for x in ff_long:
        if x not in unique_list_filters_long:
            dict_filter_long.setdefault(x, {})   

    for d_s in detlist_short:
        dict_images[d_s] = dict_filter_short

    for d_l in detlist_long:
        dict_images[d_l] = dict_filter_long

    filtlist_short = sorted(list(dict.fromkeys(dict_filter_short)))
    filtlist_long = sorted(list(dict.fromkeys(dict_filter_long)))

    if len(dict_images[d][f]) == 0:
        dict_images[d][f] = {'images': [image]}
    else:
        dict_images[d][f]['images'].append(image)

print("Available Detectors for SW channel:", detlist_short)
print("Available Detectors for LW channel:", detlist_long)
print("Available SW Filters:", filtlist_short)
print("Available LW Filters:", filtlist_long)
Downloading images
Available Detectors for SW channel: ['NRCB1']
Available Detectors for LW channel: ['NRCB5']
Available SW Filters: ['F070W', 'F115W', 'F200W']
Available LW Filters: ['F277W', 'F356W', 'F444W']
In [4]:
filters = ['F070W', 'F090W', 'F115W', 'F140M', 'F150W2', 'F150W', 'F162M', 'F164N', 'F182M',
           'F187N', 'F200W', 'F210M', 'F212N', 'F250M', 'F277W', 'F300M', 'F322W2', 'F323N',
           'F335M', 'F356W', 'F360M', 'F405N', 'F410M', 'F430M', 'F444W', 'F460M', 'F466N', 'F470N', 'F480M']

psf_fwhm = [0.987, 1.103, 1.298, 1.553, 1.628, 1.770, 1.801, 1.494, 1.990, 2.060, 2.141, 2.304, 2.341, 1.340,
            1.444, 1.585, 1.547, 1.711, 1.760, 1.830, 1.901, 2.165, 2.179, 2.300, 2.302, 2.459, 2.507, 2.535, 2.574]

zp_modA = [25.7977, 25.9686, 25.8419, 24.8878, 27.0048, 25.6536, 24.6957, 22.3073, 24.8258, 22.1775, 25.3677, 24.3296,
           22.1036, 22.7850, 23.5964, 24.8239, 23.6452, 25.3648, 20.8604, 23.5873, 24.3778, 23.4778, 20.5588,
           23.2749, 22.3584, 23.9731, 21.9502, 20.0428, 19.8869, 21.9002]

zp_modB = [25.7568, 25.9771, 25.8041, 24.8738, 26.9821, 25.6279, 24.6767, 22.2903, 24.8042, 22.1499, 25.3391, 24.2909,
           22.0574, 22.7596, 23.5011, 24.6792, 23.5769, 25.3455, 20.8631, 23.4885, 24.3883, 23.4555, 20.7007,
           23.2763, 22.4677, 24.1562, 22.0422, 20.1430, 20.0173, 22.4086]

dict_utils = {filters[i]: {'psf fwhm': psf_fwhm[i], 'VegaMAG zp modA': zp_modA[i],
                           'VegaMAG zp modB': zp_modB[i]} for i in range(len(filters))}

Select the detectors and/or filters for the analysis

If we are interested only in some filters (and/or some detectors) in the analysis, as in this example, we can select the Level-2 calibrated images from the dictionary for those filters (detectors) and analyze only those images.

In this particular example, we analyze images for filters F115W and F200W for the detector NRCB1.

In [5]:
dets_short = ['NRCB1']  # detector of interest in this example
filts_short = ['F115W', 'F200W']  # filters of interest in this example

Display the images

To check that our images do not present artifacts and can be used in the analysis, we display them using an interactive cursor that allows to shuffle through the different images for each filter.

Note for developers:

this is only a sketch of what I would like to show (I am not very familiar with ipywidgets). Would it be possible to show both filters at the same time, in a 2 window panel as in the static plot below? Or even better, have a widget control that allows to select the filters available and then use interact to cycle through the images?

In [6]:
# cell for display images using ipywidgets

def browse_images(images):
    n = len(images)

    def view_image(image):
        det = 'NRCB1'
        filt = 'F115W'
        im = fits.open(dict_images[det][filt]['images'][image])

        data_sb = im[1].data
        norm = simple_norm(data_sb, 'sqrt', percent=99.)   
        plt.figure(figsize=(10, 10))

        plt.title(filt)
        plt.imshow(data_sb, norm=norm, cmap='Greys')        
        plt.show()

    interact(view_image, image=(0, n - 1))
In [7]:
browse_images(dict_images['NRCB1']['F115W']['images'])

Note for developers:

Cell below should be removed once we finalize the interactive one above.

In [8]:
plt.figure(figsize=(14, 14))

for det in dets_short:
    for i, filt in enumerate(filts_short):

        image = fits.open(dict_images[det][filt]['images'][0])
        data_sb = image[1].data

        ax = plt.subplot(1, len(filts_short), i + 1)

        plt.xlabel("X [px]", fontdict=font2)
        plt.ylabel("Y [px]", fontdict=font2)
        plt.title(filt, fontdict=font2)
        norm = simple_norm(data_sb, 'sqrt', percent=99.)

        ax.imshow(data_sb, norm=norm, cmap='Greys')

plt.tight_layout()
findfont: Font family ['helvetica'] not found. Falling back to DejaVu Sans.

Create the PSF models

I. Create the PSF model using WebbPSF

We create a dictionary that contains the PSF created using WebbPSF for the detectors and filters selected above.

In [9]:
dict_psfs_webbpsf = {}

for det in dets_short:
    dict_psfs_webbpsf.setdefault(det, {})
    for j, filt in enumerate(filts_short):
        dict_psfs_webbpsf[det].setdefault(filt, {})

        dict_psfs_webbpsf[det][filt]['psf model grid'] = None
        dict_psfs_webbpsf[det][filt]['psf model single'] = None

The function below allows to create a single PSF or a grid of PSFs and allows to save the PSF as a fits file. The model PSF are stored by default in the psf dictionary. For the grid of PSFs, users can select the number of PSFs to be created. The PSF can be created detector sampled or oversampled (the oversample can be changed inside the function).

Note: The default source spectrum is, if pysynphot is installed, a G2V star spectrum from Castelli & Kurucz (2004). Without pysynphot, the default is a simple flat spectrum such that the same number of photons are detected at each wavelength.

In [10]:
def create_psf_model(fov=11, create_grid=False, num=9, save_psf=False, detsampled=False):

    nrc = webbpsf.NIRCam()

    nrc.detector = det 
    nrc.filter = filt

    src = webbpsf.specFromSpectralType('G5V', catalog='phoenix')
    if detsampled:
        print("Creating a detector sampled PSF")
        aa = 'detector sampled'
        fov = 21
    else:
        print("Creating a oversampled PSF")
        aa = 'oversampled'
        fov = fov

    print("Using a {field}".format(field=fov), "px fov")

    if create_grid:
        print("")
        print("Creating a grid of PSF for filter {filt} and detector {det}".format(filt=filt, det=det))
        print("")
        num = num

        if save_psf:

            outname = "./PSF_%s_samp4_G5V_fov%d_npsfs%d.fits" % (filt, fov, num)
            nrc.psf_grid(num_psfs=num, oversample=4, source=src, all_detectors=False, fov_pixels=fov,
                         save=True, outfile=outname, use_detsampled_psf=detsampled)
        else:
            grid_psf = nrc.psf_grid(num_psfs=num, oversample=4, source=src, all_detectors=False,
                                    fov_pixels=fov, use_detsampled_psf=detsampled)
            dict_psfs_webbpsf[det][filt]['psf model grid'] = grid_psf
    else:
        print("")
        print("Creating a single PSF for filter {filt} and detector {det}".format(filt=filt, det=det))
        print("")
        num = 1
        if save_psf:
            outname = "./PSF_%s_samp4_G5V_fov%d_npsfs%d.fits" % (filt, fov, num)
            nrc.psf_grid(num_psfs=num, oversample=4, source=src, all_detectors=False, fov_pixels=fov,
                         save=True, outfile=outname, use_detsampled_psf=detsampled)
        else:
            single_psf = nrc.psf_grid(num_psfs=num, oversample=4, source=src, all_detectors=False,
                                      fov_pixels=fov, use_detsampled_psf=detsampled)
            dict_psfs_webbpsf[det][filt]['psf model single'] = single_psf

    return        

Single PSF model

In [11]:
for det in dets_short:
    for filt in filts_short:
        create_psf_model(fov=11, num=25, create_grid=False, save_psf=False, detsampled=False)
Creating a oversampled PSF
Using a 11 px fov

Creating a single PSF for filter F115W and detector NRCB1


Running instrument: NIRCam, filter: F115W
  Running detector: NRCB1
    Position 1/1: (1023, 1023) pixels
Creating a oversampled PSF
Using a 11 px fov

Creating a single PSF for filter F200W and detector NRCB1


Running instrument: NIRCam, filter: F200W
  Running detector: NRCB1
    Position 1/1: (1023, 1023) pixels

Display the single PSF models

In [12]:
plt.figure(figsize=(14, 14))

for det in dets_short:
    for i, filt in enumerate(filts_short):
        ax = plt.subplot(1, 2, i + 1)

        norm_epsf = simple_norm(dict_psfs_webbpsf[det][filt]['psf model single'].data[0], 'log', percent=99.)
        ax.set_title(filt, fontsize=40)
        ax.imshow(dict_psfs_webbpsf[det][filt]['psf model single'].data[0], norm=norm_epsf)
        ax.set_xlabel('X [px]', fontsize=30)
        ax.set_ylabel('Y [px]', fontsize=30)
plt.tight_layout()

PSF grid

In [13]:
for det in dets_short:
    for filt in filts_short:
        create_psf_model(fov=11, num=25, create_grid=True, save_psf=False, detsampled=False)
Creating a oversampled PSF
Using a 11 px fov

Creating a grid of PSF for filter F115W and detector NRCB1


Running instrument: NIRCam, filter: F115W
  Running detector: NRCB1
    Position 1/25: (0, 0) pixels
    Position 2/25: (0, 512) pixels
    Position 3/25: (0, 1024) pixels
    Position 4/25: (0, 1535) pixels
    Position 5/25: (0, 2047) pixels
    Position 6/25: (512, 0) pixels
    Position 7/25: (512, 512) pixels
    Position 8/25: (512, 1024) pixels
    Position 9/25: (512, 1535) pixels
    Position 10/25: (512, 2047) pixels
    Position 11/25: (1024, 0) pixels
    Position 12/25: (1024, 512) pixels
    Position 13/25: (1024, 1024) pixels
    Position 14/25: (1024, 1535) pixels
    Position 15/25: (1024, 2047) pixels
    Position 16/25: (1535, 0) pixels
    Position 17/25: (1535, 512) pixels
    Position 18/25: (1535, 1024) pixels
    Position 19/25: (1535, 1535) pixels
    Position 20/25: (1535, 2047) pixels
    Position 21/25: (2047, 0) pixels
    Position 22/25: (2047, 512) pixels
    Position 23/25: (2047, 1024) pixels
    Position 24/25: (2047, 1535) pixels
    Position 25/25: (2047, 2047) pixels
Creating a oversampled PSF
Using a 11 px fov

Creating a grid of PSF for filter F200W and detector NRCB1


Running instrument: NIRCam, filter: F200W
  Running detector: NRCB1
    Position 1/25: (0, 0) pixels
    Position 2/25: (0, 512) pixels
    Position 3/25: (0, 1024) pixels
    Position 4/25: (0, 1535) pixels
    Position 5/25: (0, 2047) pixels
    Position 6/25: (512, 0) pixels
    Position 7/25: (512, 512) pixels
    Position 8/25: (512, 1024) pixels
    Position 9/25: (512, 1535) pixels
    Position 10/25: (512, 2047) pixels
    Position 11/25: (1024, 0) pixels
    Position 12/25: (1024, 512) pixels
    Position 13/25: (1024, 1024) pixels
    Position 14/25: (1024, 1535) pixels
    Position 15/25: (1024, 2047) pixels
    Position 16/25: (1535, 0) pixels
    Position 17/25: (1535, 512) pixels
    Position 18/25: (1535, 1024) pixels
    Position 19/25: (1535, 1535) pixels
    Position 20/25: (1535, 2047) pixels
    Position 21/25: (2047, 0) pixels
    Position 22/25: (2047, 512) pixels
    Position 23/25: (2047, 1024) pixels
    Position 24/25: (2047, 1535) pixels
    Position 25/25: (2047, 2047) pixels

Display the PSFs grid

We show for 1 filter (F115W) the grid of PSFs and the difference from the mean

In [14]:
webbpsf.gridded_library.display_psf_grid(dict_psfs_webbpsf[dets_short[0]][filts_short[0]]['psf model grid'],
                                         zoom_in=False, figsize=(14, 14))
/tmp/nbcollection-ci/scanner-build-dir/jdat_notebooks/psf_photometry/lib/python3.8/site-packages/webbpsf/gridded_library.py:509: MatplotlibDeprecationWarning: Passing parameters norm and vmin/vmax simultaneously is deprecated since 3.3 and will become an error two minor releases later. Please pass vmin/vmax directly to the norm when creating it.
  axes[n-1-iy, ix].imshow(data[i], vmax=vmax, vmin=vmin, norm=norm)

II. Create the PSF model building an Effective PSF (ePSF)

More information on the PhotUtils Effective PSF can be found here.

  • Select the stars from the images we want to use for building the PSF. We use the DAOStarFinder function to find bright stars in the images (setting a high detection threshold). DAOStarFinder detects stars in an image using the DAOFIND (Stetson 1987) algorithm. DAOFIND searches images for local density maxima that have a peak amplitude greater than threshold (approximately; threshold is applied to a convolved image) and have a size and shape similar to the defined 2D Gaussian kernel. \ Note: The threshold and the maximum distance to the closest neighbour depend on the user science case (i.e.; number of stars in the field of view, crowding, number of bright sources, minimum number of stars required to build the ePSF, etc.) and must be modified accordingly.
  • Build the effective PSF (excluding objects for which the bounding box exceed the detector edge) using EPSBuilder function.

We create a dictionary that contains the effective PSF for the detectors and filters selected above.

In [15]:
dict_psfs_epsf = {}

for det in dets_short:
    dict_psfs_epsf.setdefault(det, {})
    for j, filt in enumerate(filts_short):
        dict_psfs_epsf[det].setdefault(filt, {})

        dict_psfs_epsf[det][filt]['table psf stars'] = {}
        dict_psfs_epsf[det][filt]['epsf single'] = {}
        dict_psfs_epsf[det][filt]['epsf grid'] = {}

        for i in np.arange(0, len(dict_images[det][filt]['images']), 1):

            dict_psfs_epsf[det][filt]['table psf stars'][i + 1] = None
            dict_psfs_epsf[det][filt]['epsf single'][i + 1] = None
            dict_psfs_epsf[det][filt]['epsf grid'][i + 1] = None

Note that the unit of the Level-2 and Level-3 Images from the pipeline is MJy/sr (hence a surface brightness). The actual unit of the image can be checked from the header keyword BUNIT. The scalar conversion constant is copied to the header keyword PHOTMJSR, which gives the conversion from DN/s to megaJy/steradian. For our analysis we revert back to DN/s.

In [16]:
def find_stars_epsf(det='NRCA1', filt='F070W', dist_sel=False):

    bkgrms = MADStdBackgroundRMS()
    mmm_bkg = MMMBackground()

    image = fits.open(dict_images[det][filt]['images'][i])
    data_sb = image[1].data
    imh = image[1].header

    print("Finding PSF stars on image {number} of filter {f}, detector {d}".format(number=i + 1, f=filt, d=det))

    data = data_sb / imh['PHOTMJSR']
    print("Conversion factor from {units} to DN/s for filter {f}:".format(units=imh['BUNIT'], f=filt), imh['PHOTMJSR'])

    sigma_psf = dict_utils[filt]['psf fwhm']

    print("FWHM for the filter {f}:".format(f=filt), sigma_psf, "px")

    std = bkgrms(data)
    bkg = mmm_bkg(data)
    daofind = DAOStarFinder(threshold=th[j] * std + bkg, fwhm=sigma_psf, roundhi=1.0, roundlo=-1.0,
                            sharplo=0.30, sharphi=1.40)

    psf_stars = daofind(data)
    dict_psfs_epsf[det][filt]['table psf stars'][i + 1] = psf_stars
    
    if dist_sel:

        print("")
        print("Calculating closest neigbhour distance")

        d = []

        daofind_tot = DAOStarFinder(threshold=10 * std + bkg, fwhm=sigma_psf, roundhi=1.0, roundlo=-1.0,
                                    sharplo=0.30, sharphi=1.40)

        stars_tot = daofind_tot(data)

        x_tot = stars_tot['xcentroid']
        y_tot = stars_tot['ycentroid']

        for xx, yy in zip(psf_stars['xcentroid'], psf_stars['ycentroid']):

            sep = []
            dist = np.sqrt((x_tot - xx)**2 + (y_tot - yy)**2)
            sep = np.sort(dist)[1:2][0]
            d.append(sep)

        psf_stars['min distance'] = d
        mask_dist = (psf_stars['min distance'] > min_sep[j])

        psf_stars = psf_stars[mask_dist]

        dict_psfs_epsf[det][filt]['table psf stars'][i + 1] = psf_stars

        print("Minimum distance required:", min_sep[j], "px")
        print("")
        print("Number of isolated sources found in the image used to build ePSF for {f}:".format(f=filt), len(psf_stars))
        print("-----------------------------------------------------")
        print("")
    else:
        print("")
        print("Number of sources used to build ePSF for {f}:".format(f=filt), len(psf_stars))
        print("--------------------------------------------")
        print("")
In [17]:
tic = time.perf_counter()

th = [700, 500]  # threshold level for the two filters (length must match number of filters analyzed)
min_sep = [10, 10]  # minimum separation acceptable for ePSF stars from closest neighbour

for det in dets_short:
    for j, filt in enumerate(filts_short):
        for i in np.arange(0, len(dict_images[det][filt]['images']), 1):

            find_stars_epsf(det=det, filt=filt, dist_sel=False)

toc = time.perf_counter()

print("Elapsed Time for finding stars:", toc - tic)
Finding PSF stars on image 1 of filter F115W, detector NRCB1
Conversion factor from MJy/sr to DN/s for filter F115W: 3.821892261505127
FWHM for the filter F115W: 1.298 px

Number of sources used to build ePSF for F115W: 1306
--------------------------------------------

Finding PSF stars on image 2 of filter F115W, detector NRCB1
Conversion factor from MJy/sr to DN/s for filter F115W: 3.821892261505127
FWHM for the filter F115W: 1.298 px

Number of sources used to build ePSF for F115W: 1324
--------------------------------------------

Finding PSF stars on image 3 of filter F115W, detector NRCB1
Conversion factor from MJy/sr to DN/s for filter F115W: 3.821892261505127
FWHM for the filter F115W: 1.298 px

Number of sources used to build ePSF for F115W: 1307
--------------------------------------------

Finding PSF stars on image 4 of filter F115W, detector NRCB1
Conversion factor from MJy/sr to DN/s for filter F115W: 3.821892261505127
FWHM for the filter F115W: 1.298 px

Number of sources used to build ePSF for F115W: 1316
--------------------------------------------

Finding PSF stars on image 1 of filter F200W, detector NRCB1
Conversion factor from MJy/sr to DN/s for filter F200W: 2.564082860946655
FWHM for the filter F200W: 2.141 px

Number of sources used to build ePSF for F200W: 1276
--------------------------------------------

Finding PSF stars on image 2 of filter F200W, detector NRCB1
Conversion factor from MJy/sr to DN/s for filter F200W: 2.564082860946655
FWHM for the filter F200W: 2.141 px

Number of sources used to build ePSF for F200W: 1287
--------------------------------------------

Finding PSF stars on image 3 of filter F200W, detector NRCB1
Conversion factor from MJy/sr to DN/s for filter F200W: 2.564082860946655
FWHM for the filter F200W: 2.141 px

Number of sources used to build ePSF for F200W: 1291
--------------------------------------------

Finding PSF stars on image 4 of filter F200W, detector NRCB1
Conversion factor from MJy/sr to DN/s for filter F200W: 2.564082860946655
FWHM for the filter F200W: 2.141 px

Number of sources used to build ePSF for F200W: 1278
--------------------------------------------

Elapsed Time for finding stars: 33.847110691000125

II. Build Effective PSF

In [18]:
def build_epsf(det='NRCA1', filt='F070W'):
    
    mmm_bkg = MMMBackground()
    
    image = fits.open(dict_images[det][filt]['images'][i])
    data_sb = image[1].data
    imh = image[1].header

    data = data_sb / imh['PHOTMJSR']

    hsize = (sizes[j] - 1) / 2

    x = dict_psfs_epsf[det][filt]['table psf stars'][i + 1]['xcentroid']
    y = dict_psfs_epsf[det][filt]['table psf stars'][i + 1]['ycentroid']
    mask = ((x > hsize) & (x < (data.shape[1] - 1 - hsize)) & (y > hsize) & (y < (data.shape[0] - 1 - hsize)))

    stars_tbl = Table()
    stars_tbl['x'] = x[mask]
    stars_tbl['y'] = y[mask]

    bkg = mmm_bkg(data)

    data_bkgsub = data.copy()

    data_bkgsub -= bkg

    nddata = NDData(data=data_bkgsub)
    stars = extract_stars(nddata, stars_tbl, size=sizes[j])

    print("Creating ePSF for image {number} of filter {f}, detector {d}".format(number=i + 1, f=filt, d=det))

    epsf_builder = EPSFBuilder(oversampling=oversample, maxiters=3, progress_bar=False)

    epsf, fitted_stars = epsf_builder(stars)
    dict_psfs_epsf[det][filt]['epsf single'][i + 1] = epsf

Note: here we limit the maximum number of iterations to 3 (to limit it’s run time), but in practice one should use about 10 or more iterations.

In [19]:
tic = time.perf_counter()

sizes = [11, 11]  # size of the cutout (extract region) for each PSF star - must match number of filters analyzed
oversample = 4

for det in dets_short:
    for j, filt in enumerate(filts_short):
        for i in np.arange(0, len(dict_images[det][filt]['images']), 1):
            build_epsf(det=det, filt=filt)

toc = time.perf_counter()

print("Time to build the Effective PSF:", toc - tic)                  
Creating ePSF for image 1 of filter F115W, detector NRCB1
Creating ePSF for image 2 of filter F115W, detector NRCB1
Creating ePSF for image 3 of filter F115W, detector NRCB1
Creating ePSF for image 4 of filter F115W, detector NRCB1
Creating ePSF for image 1 of filter F200W, detector NRCB1
WARNING: The star at (2024.6583712618988, 303.05613687467087) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
WARNING: The star at (1287.6938989443017, 184.04681924060748) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
WARNING: The star at (2024.6583712618988, 303.05613687467087) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
WARNING: The star at (1385.8600398323995, 733.8972484228361) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
WARNING: The star at (26.284959747891065, 926.5001627023972) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
WARNING: The star at (1942.2238557129556, 1446.3283575899338) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
WARNING: The star at (1356.7816503557121, 1694.7562979221789) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
WARNING: The star at (1913.8036243641827, 2036.2020641439606) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
Creating ePSF for image 2 of filter F200W, detector NRCB1
WARNING: The star at (1254.1534926597653, 37.344944331531984) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
WARNING: The star at (2013.6414870311062, 312.81837167426096) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
WARNING: The star at (31.360541297377488, 933.8276852413142) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
WARNING: The star at (271.2395713411656, 1692.6433967443513) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
WARNING: The star at (1918.5987179055912, 2040.976602498569) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
Creating ePSF for image 3 of filter F200W, detector NRCB1
WARNING: The star at (811.4333523945055, 595.3465086660839) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
WARNING: The star at (1366.8241155624034, 231.93661176140287) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
WARNING: The star at (1366.715469929931, 232.74382880584332) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
WARNING: The star at (2010.5398162755582, 315.48809568080753) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
WARNING: The star at (378.35911922042163, 577.7662913926318) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
WARNING: The star at (811.4333523945055, 595.3465086660839) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
WARNING: The star at (970.297816824831, 816.1998334840716) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
WARNING: The star at (1212.6776286738234, 1638.0921925173056) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
Creating ePSF for image 4 of filter F200W, detector NRCB1
WARNING: The star at (975.8053091205506, 810.4505240833056) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
WARNING: The star at (1371.179005239543, 227.01540958771997) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
WARNING: The star at (1371.7935197529089, 227.0491003308669) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
WARNING: The star at (2031.6502442013732, 304.50771304247894) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
WARNING: The star at (2015.8337608261713, 309.5029288861399) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
WARNING: The star at (380.0252804162881, 573.3385577797417) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
WARNING: The star at (975.8053091205506, 810.4505240833056) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
WARNING: The star at (1252.4648604612446, 883.7542036883244) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
WARNING: The star at (1217.2316895541658, 1632.3846435471307) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]
Time to build the Effective PSF: 230.07732273899455
WARNING: The star at (1921.778778548655, 2037.860473681066) cannot be fit because its fitting region extends beyond the star cutout image. [photutils.psf.epsf]

Display the ePSFs

We display only 1 ePSF for each filter

In [20]:
plt.figure(figsize=(14, 14))

for det in dets_short:
    for i, filt in enumerate(filts_short):
        ax = plt.subplot(1, 2, i + 1)

        norm_epsf = simple_norm(dict_psfs_epsf[det][filt]['epsf single'][i + 1].data, 'log', percent=99.)
        plt.title(filt, fontsize=30)
        ax.imshow(dict_psfs_epsf[det][filt]['epsf single'][i + 1].data, norm=norm_epsf)

Work in Progress - Build a grid of effective PSF

Two functions:

  • count PSF stars in the grid
  • create a gridded ePSF

The purpose of the first function is to count how many good PSF stars are in each sub-region defined by the grid number N. The function should start from the number provided by the user and iterate until the minimum grid size 2x2. Depending on the number of PSF stars that the users want in each cell of the grid, they can choose the appropriate grid size or modify the threshold values for the stars detection, selected when creating the single ePSF (in the Finding stars cell above).

The second function creates a grid of PSFs with EPSFBuilder. The function will return a a GriddedEPSFModel object containing a 3D array of N × n × n. The 3D array represents the N number of 2D n × n ePSFs created. It should include a grid_xypos key which will state the position of the PSF on the detector for each of the PSFs. The order of the tuples in grid_xypos refers to the number the PSF is in the 3D array.

I. Counting PSF stars in each region of the grid

In [21]:
def count_PSFstars_grid(grid_points=5, size=15, min_numpsf=40):

    num_grid_calc = np.arange(2, grid_points + 1, 1)
    num_grid_calc = num_grid_calc[::-1]

    for num in num_grid_calc:
        print("Calculating the number of PSF stars in a %d x %d grid:" % (num, num))
        print("")

        image = fits.open(dict_images[det][filt]['images'][i])
        data_sb = image[1].data

        points = np.int16((data_sb.shape[0] / num) / 2)
        x_center = np.arange(points, 2 * points * (num), 2 * points)
        y_center = np.arange(points, 2 * points * (num), 2 * points)

        centers = np.array(np.meshgrid(x_center, y_center)).T.reshape(-1, 2)

        for n, val in enumerate(centers):

            x = dict_psfs_epsf[det][filt]['table psf stars'][i + 1]['xcentroid']
            y = dict_psfs_epsf[det][filt]['table psf stars'][i + 1]['ycentroid']
            flux = dict_psfs_epsf[det][filt]['table psf stars'][i + 1]['flux']

            half_size = (size - 1) / 2

            lim1 = val[0] - points + half_size
            lim2 = val[0] + points - half_size
            lim3 = val[1] - points + half_size
            lim4 = val[1] + points - half_size

            test = (x > lim1) & (x < lim2) & (y > lim3) & (y < lim4)

            # if np.count_nonzero(test) < min_numpsf:
            # raise ValueError("Not enough PSF stars in all the cells (> %d): Decrease your grid size or the minimum number of PSF stars in each cell or change parameters in the finder" %(min_numpsf))
            if np.count_nonzero(test) < min_numpsf:
                print("Center Coordinates of grid cell %d are (%d, %d) --- Not enough PSF stars in the cell (number of PSF stars < %d)" % (i + 1, val[0], val[1], min_numpsf))

            else:
                print("Center Coordinate of grid cell %d are (%d, %d) --- Number of PSF stars:" % (n + 1, val[0], val[1]), np.count_nonzero(test))                
        print("")
In [22]:
for det in dets_short:
    for j, filt in enumerate(filts_short):
        for i in np.arange(0, len(dict_images[det][filt]['images']), 1):

            print("Analyzing image {number} of filter {f}, detector {d} ".format(number=i + 1, f=filt, d=det))
            print("")

            count_PSFstars_grid(grid_points=5, size=15, min_numpsf=40)
Analyzing image 1 of filter F115W, detector NRCB1 

Calculating the number of PSF stars in a 5 x 5 grid:

Center Coordinate of grid cell 1 are (204, 204) --- Number of PSF stars: 72
Center Coordinate of grid cell 2 are (204, 612) --- Number of PSF stars: 46
Center Coordinate of grid cell 3 are (204, 1020) --- Number of PSF stars: 51
Center Coordinate of grid cell 4 are (204, 1428) --- Number of PSF stars: 48
Center Coordinate of grid cell 5 are (204, 1836) --- Number of PSF stars: 51
Center Coordinate of grid cell 6 are (612, 204) --- Number of PSF stars: 55
Center Coordinate of grid cell 7 are (612, 612) --- Number of PSF stars: 51
Center Coordinate of grid cell 8 are (612, 1020) --- Number of PSF stars: 45
Center Coordinate of grid cell 9 are (612, 1428) --- Number of PSF stars: 40
Center Coordinate of grid cell 10 are (612, 1836) --- Number of PSF stars: 57
Center Coordinate of grid cell 11 are (1020, 204) --- Number of PSF stars: 47
Center Coordinate of grid cell 12 are (1020, 612) --- Number of PSF stars: 50
Center Coordinate of grid cell 13 are (1020, 1020) --- Number of PSF stars: 55
Center Coordinates of grid cell 1 are (1020, 1428) --- Not enough PSF stars in the cell (number of PSF stars < 40)
Center Coordinate of grid cell 15 are (1020, 1836) --- Number of PSF stars: 43
Center Coordinate of grid cell 16 are (1428, 204) --- Number of PSF stars: 50
Center Coordinate of grid cell 17 are (1428, 612) --- Number of PSF stars: 44
Center Coordinates of grid cell 1 are (1428, 1020) --- Not enough PSF stars in the cell (number of PSF stars < 40)
Center Coordinate of grid cell 19 are (1428, 1428) --- Number of PSF stars: 47
Center Coordinate of grid cell 20 are (1428, 1836) --- Number of PSF stars: 50
Center Coordinate of grid cell 21 are (1836, 204) --- Number of PSF stars: 45
Center Coordinate of grid cell 22 are (1836, 612) --- Number of PSF stars: 50
Center Coordinate of grid cell 23 are (1836, 1020) --- Number of PSF stars: 54
Center Coordinate of grid cell 24 are (1836, 1428) --- Number of PSF stars: 44
Center Coordinate of grid cell 25 are (1836, 1836) --- Number of PSF stars: 40

Calculating the number of PSF stars in a 4 x 4 grid:

Center Coordinate of grid cell 1 are (256, 256) --- Number of PSF stars: 122
Center Coordinate of grid cell 2 are (256, 768) --- Number of PSF stars: 73
Center Coordinate of grid cell 3 are (256, 1280) --- Number of PSF stars: 70
Center Coordinate of grid cell 4 are (256, 1792) --- Number of PSF stars: 90
Center Coordinate of grid cell 5 are (768, 256) --- Number of PSF stars: 84
Center Coordinate of grid cell 6 are (768, 768) --- Number of PSF stars: 75
Center Coordinate of grid cell 7 are (768, 1280) --- Number of PSF stars: 84
Center Coordinate of grid cell 8 are (768, 1792) --- Number of PSF stars: 81
Center Coordinate of grid cell 9 are (1280, 256) --- Number of PSF stars: 77
Center Coordinate of grid cell 10 are (1280, 768) --- Number of PSF stars: 87
Center Coordinate of grid cell 11 are (1280, 1280) --- Number of PSF stars: 59
Center Coordinate of grid cell 12 are (1280, 1792) --- Number of PSF stars: 75
Center Coordinate of grid cell 13 are (1792, 256) --- Number of PSF stars: 75
Center Coordinate of grid cell 14 are (1792, 768) --- Number of PSF stars: 64
Center Coordinate of grid cell 15 are (1792, 1280) --- Number of PSF stars: 76
Center Coordinate of grid cell 16 are (1792, 1792) --- Number of PSF stars: 67

Calculating the number of PSF stars in a 3 x 3 grid:

Center Coordinate of grid cell 1 are (341, 341) --- Number of PSF stars: 186
Center Coordinate of grid cell 2 are (341, 1023) --- Number of PSF stars: 129
Center Coordinate of grid cell 3 are (341, 1705) --- Number of PSF stars: 149
Center Coordinate of grid cell 4 are (1023, 341) --- Number of PSF stars: 132
Center Coordinate of grid cell 5 are (1023, 1023) --- Number of PSF stars: 151
Center Coordinate of grid cell 6 are (1023, 1705) --- Number of PSF stars: 132
Center Coordinate of grid cell 7 are (1705, 341) --- Number of PSF stars: 132
Center Coordinate of grid cell 8 are (1705, 1023) --- Number of PSF stars: 118
Center Coordinate of grid cell 9 are (1705, 1705) --- Number of PSF stars: 126

Calculating the number of PSF stars in a 2 x 2 grid:

Center Coordinate of grid cell 1 are (512, 512) --- Number of PSF stars: 359
Center Coordinate of grid cell 2 are (512, 1536) --- Number of PSF stars: 331
Center Coordinate of grid cell 3 are (1536, 512) --- Number of PSF stars: 307
Center Coordinate of grid cell 4 are (1536, 1536) --- Number of PSF stars: 286

Analyzing image 2 of filter F115W, detector NRCB1 

Calculating the number of PSF stars in a 5 x 5 grid:

Center Coordinate of grid cell 1 are (204, 204) --- Number of PSF stars: 74
Center Coordinate of grid cell 2 are (204, 612) --- Number of PSF stars: 49
Center Coordinate of grid cell 3 are (204, 1020) --- Number of PSF stars: 50
Center Coordinate of grid cell 4 are (204, 1428) --- Number of PSF stars: 45
Center Coordinate of grid cell 5 are (204, 1836) --- Number of PSF stars: 48
Center Coordinate of grid cell 6 are (612, 204) --- Number of PSF stars: 59
Center Coordinate of grid cell 7 are (612, 612) --- Number of PSF stars: 53
Center Coordinate of grid cell 8 are (612, 1020) --- Number of PSF stars: 43
Center Coordinate of grid cell 9 are (612, 1428) --- Number of PSF stars: 46
Center Coordinate of grid cell 10 are (612, 1836) --- Number of PSF stars: 62
Center Coordinate of grid cell 11 are (1020, 204) --- Number of PSF stars: 50
Center Coordinate of grid cell 12 are (1020, 612) --- Number of PSF stars: 49
Center Coordinate of grid cell 13 are (1020, 1020) --- Number of PSF stars: 55
Center Coordinate of grid cell 14 are (1020, 1428) --- Number of PSF stars: 42
Center Coordinate of grid cell 15 are (1020, 1836) --- Number of PSF stars: 42
Center Coordinate of grid cell 16 are (1428, 204) --- Number of PSF stars: 55
Center Coordinate of grid cell 17 are (1428, 612) --- Number of PSF stars: 43
Center Coordinates of grid cell 2 are (1428, 1020) --- Not enough PSF stars in the cell (number of PSF stars < 40)
Center Coordinate of grid cell 19 are (1428, 1428) --- Number of PSF stars: 47
Center Coordinate of grid cell 20 are (1428, 1836) --- Number of PSF stars: 50
Center Coordinate of grid cell 21 are (1836, 204) --- Number of PSF stars: 41
Center Coordinate of grid cell 22 are (1836, 612) --- Number of PSF stars: 47
Center Coordinate of grid cell 23 are (1836, 1020) --- Number of PSF stars: 59
Center Coordinate of grid cell 24 are (1836, 1428) --- Number of PSF stars: 44
Center Coordinates of grid cell 2 are (1836, 1836) --- Not enough PSF stars in the cell (number of PSF stars < 40)

Calculating the number of PSF stars in a 4 x 4 grid:

Center Coordinate of grid cell 1 are (256, 256) --- Number of PSF stars: 138
Center Coordinate of grid cell 2 are (256, 768) --- Number of PSF stars: 79
Center Coordinate of grid cell 3 are (256, 1280) --- Number of PSF stars: 75
Center Coordinate of grid cell 4 are (256, 1792) --- Number of PSF stars: 82
Center Coordinate of grid cell 5 are (768, 256) --- Number of PSF stars: 84
Center Coordinate of grid cell 6 are (768, 768) --- Number of PSF stars: 73
Center Coordinate of grid cell 7 are (768, 1280) --- Number of PSF stars: 84
Center Coordinate of grid cell 8 are (768, 1792) --- Number of PSF stars: 86
Center Coordinate of grid cell 9 are (1280, 256) --- Number of PSF stars: 80
Center Coordinate of grid cell 10 are (1280, 768) --- Number of PSF stars: 82
Center Coordinate of grid cell 11 are (1280, 1280) --- Number of PSF stars: 61
Center Coordinate of grid cell 12 are (1280, 1792) --- Number of PSF stars: 69
Center Coordinate of grid cell 13 are (1792, 256) --- Number of PSF stars: 72
Center Coordinate of grid cell 14 are (1792, 768) --- Number of PSF stars: 62
Center Coordinate of grid cell 15 are (1792, 1280) --- Number of PSF stars: 70
Center Coordinate of grid cell 16 are (1792, 1792) --- Number of PSF stars: 73

Calculating the number of PSF stars in a 3 x 3 grid:

Center Coordinate of grid cell 1 are (341, 341) --- Number of PSF stars: 201
Center Coordinate of grid cell 2 are (341, 1023) --- Number of PSF stars: 134
Center Coordinate of grid cell 3 are (341, 1705) --- Number of PSF stars: 150
Center Coordinate of grid cell 4 are (1023, 341) --- Number of PSF stars: 139
Center Coordinate of grid cell 5 are (1023, 1023) --- Number of PSF stars: 143
Center Coordinate of grid cell 6 are (1023, 1705) --- Number of PSF stars: 133
Center Coordinate of grid cell 7 are (1705, 341) --- Number of PSF stars: 126
Center Coordinate of grid cell 8 are (1705, 1023) --- Number of PSF stars: 123
Center Coordinate of grid cell 9 are (1705, 1705) --- Number of PSF stars: 125

Calculating the number of PSF stars in a 2 x 2 grid:

Center Coordinate of grid cell 1 are (512, 512) --- Number of PSF stars: 378
Center Coordinate of grid cell 2 are (512, 1536) --- Number of PSF stars: 333
Center Coordinate of grid cell 3 are (1536, 512) --- Number of PSF stars: 303
Center Coordinate of grid cell 4 are (1536, 1536) --- Number of PSF stars: 280

Analyzing image 3 of filter F115W, detector NRCB1 

Calculating the number of PSF stars in a 5 x 5 grid:

Center Coordinate of grid cell 1 are (204, 204) --- Number of PSF stars: 73
Center Coordinate of grid cell 2 are (204, 612) --- Number of PSF stars: 50
Center Coordinate of grid cell 3 are (204, 1020) --- Number of PSF stars: 47
Center Coordinate of grid cell 4 are (204, 1428) --- Number of PSF stars: 45
Center Coordinate of grid cell 5 are (204, 1836) --- Number of PSF stars: 46
Center Coordinate of grid cell 6 are (612, 204) --- Number of PSF stars: 57
Center Coordinate of grid cell 7 are (612, 612) --- Number of PSF stars: 48
Center Coordinate of grid cell 8 are (612, 1020) --- Number of PSF stars: 47
Center Coordinate of grid cell 9 are (612, 1428) --- Number of PSF stars: 46
Center Coordinate of grid cell 10 are (612, 1836) --- Number of PSF stars: 58
Center Coordinate of grid cell 11 are (1020, 204) --- Number of PSF stars: 46
Center Coordinate of grid cell 12 are (1020, 612) --- Number of PSF stars: 52
Center Coordinate of grid cell 13 are (1020, 1020) --- Number of PSF stars: 56
Center Coordinate of grid cell 14 are (1020, 1428) --- Number of PSF stars: 44
Center Coordinates of grid cell 3 are (1020, 1836) --- Not enough PSF stars in the cell (number of PSF stars < 40)
Center Coordinate of grid cell 16 are (1428, 204) --- Number of PSF stars: 48
Center Coordinate of grid cell 17 are (1428, 612) --- Number of PSF stars: 41
Center Coordinates of grid cell 3 are (1428, 1020) --- Not enough PSF stars in the cell (number of PSF stars < 40)
Center Coordinate of grid cell 19 are (1428, 1428) --- Number of PSF stars: 51
Center Coordinate of grid cell 20 are (1428, 1836) --- Number of PSF stars: 53
Center Coordinate of grid cell 21 are (1836, 204) --- Number of PSF stars: 43
Center Coordinate of grid cell 22 are (1836, 612) --- Number of PSF stars: 49
Center Coordinate of grid cell 23 are (1836, 1020) --- Number of PSF stars: 52
Center Coordinate of grid cell 24 are (1836, 1428) --- Number of PSF stars: 46
Center Coordinates of grid cell 3 are (1836, 1836) --- Not enough PSF stars in the cell (number of PSF stars < 40)

Calculating the number of PSF stars in a 4 x 4 grid:

Center Coordinate of grid cell 1 are (256, 256) --- Number of PSF stars: 128
Center Coordinate of grid cell 2 are (256, 768) --- Number of PSF stars: 77
Center Coordinate of grid cell 3 are (256, 1280) --- Number of PSF stars: 69
Center Coordinate of grid cell 4 are (256, 1792) --- Number of PSF stars: 81
Center Coordinate of grid cell 5 are (768, 256) --- Number of PSF stars: 81
Center Coordinate of grid cell 6 are (768, 768) --- Number of PSF stars: 73
Center Coordinate of grid cell 7 are (768, 1280) --- Number of PSF stars: 87
Center Coordinate of grid cell 8 are (768, 1792) --- Number of PSF stars: 79
Center Coordinate of grid cell 9 are (1280, 256) --- Number of PSF stars: 75
Center Coordinate of grid cell 10 are (1280, 768) --- Number of PSF stars: 82
Center Coordinate of grid cell 11 are (1280, 1280) --- Number of PSF stars: 65
Center Coordinate of grid cell 12 are (1280, 1792) --- Number of PSF stars: 74
Center Coordinate of grid cell 13 are (1792, 256) --- Number of PSF stars: 71
Center Coordinate of grid cell 14 are (1792, 768) --- Number of PSF stars: 65
Center Coordinate of grid cell 15 are (1792, 1280) --- Number of PSF stars: 75
Center Coordinate of grid cell 16 are (1792, 1792) --- Number of PSF stars: 69

Calculating the number of PSF stars in a 3 x 3 grid:

Center Coordinate of grid cell 1 are (341, 341) --- Number of PSF stars: 191
Center Coordinate of grid cell 2 are (341, 1023) --- Number of PSF stars: 127
Center Coordinate of grid cell 3 are (341, 1705) --- Number of PSF stars: 147
Center Coordinate of grid cell 4 are (1023, 341) --- Number of PSF stars: 135
Center Coordinate of grid cell 5 are (1023, 1023) --- Number of PSF stars: 140
Center Coordinate of grid cell 6 are (1023, 1705) --- Number of PSF stars: 133
Center Coordinate of grid cell 7 are (1705, 341) --- Number of PSF stars: 131
Center Coordinate of grid cell 8 are (1705, 1023) --- Number of PSF stars: 120
Center Coordinate of grid cell 9 are (1705, 1705) --- Number of PSF stars: 131

Calculating the number of PSF stars in a 2 x 2 grid:

Center Coordinate of grid cell 1 are (512, 512) --- Number of PSF stars: 366
Center Coordinate of grid cell 2 are (512, 1536) --- Number of PSF stars: 320
Center Coordinate of grid cell 3 are (1536, 512) --- Number of PSF stars: 297
Center Coordinate of grid cell 4 are (1536, 1536) --- Number of PSF stars: 292

Analyzing image 4 of filter F115W, detector NRCB1 

Calculating the number of PSF stars in a 5 x 5 grid:

Center Coordinate of grid cell 1 are (204, 204) --- Number of PSF stars: 66
Center Coordinate of grid cell 2 are (204, 612) --- Number of PSF stars: 46
Center Coordinate of grid cell 3 are (204, 1020) --- Number of PSF stars: 47
Center Coordinate of grid cell 4 are (204, 1428) --- Number of PSF stars: 48
Center Coordinate of grid cell 5 are (204, 1836) --- Number of PSF stars: 51
Center Coordinate of grid cell 6 are (612, 204) --- Number of PSF stars: 59
Center Coordinate of grid cell 7 are (612, 612) --- Number of PSF stars: 52
Center Coordinate of grid cell 8 are (612, 1020) --- Number of PSF stars: 47
Center Coordinate of grid cell 9 are (612, 1428) --- Number of PSF stars: 44
Center Coordinate of grid cell 10 are (612, 1836) --- Number of PSF stars: 63
Center Coordinate of grid cell 11 are (1020, 204) --- Number of PSF stars: 47
Center Coordinate of grid cell 12 are (1020, 612) --- Number of PSF stars: 50
Center Coordinate of grid cell 13 are (1020, 1020) --- Number of PSF stars: 56
Center Coordinate of grid cell 14 are (1020, 1428) --- Number of PSF stars: 41
Center Coordinate of grid cell 15 are (1020, 1836) --- Number of PSF stars: 41
Center Coordinate of grid cell 16 are (1428, 204) --- Number of PSF stars: 54
Center Coordinate of grid cell 17 are (1428, 612) --- Number of PSF stars: 45
Center Coordinate of grid cell 18 are (1428, 1020) --- Number of PSF stars: 40
Center Coordinate of grid cell 19 are (1428, 1428) --- Number of PSF stars: 50
Center Coordinate of grid cell 20 are (1428, 1836) --- Number of PSF stars: 50
Center Coordinate of grid cell 21 are (1836, 204) --- Number of PSF stars: 42
Center Coordinate of grid cell 22 are (1836, 612) --- Number of PSF stars: 51
Center Coordinate of grid cell 23 are (1836, 1020) --- Number of PSF stars: 55
Center Coordinate of grid cell 24 are (1836, 1428) --- Number of PSF stars: 42
Center Coordinates of grid cell 4 are (1836, 1836) --- Not enough PSF stars in the cell (number of PSF stars < 40)

Calculating the number of PSF stars in a 4 x 4 grid:

Center Coordinate of grid cell 1 are (256, 256) --- Number of PSF stars: 121
Center Coordinate of grid cell 2 are (256, 768) --- Number of PSF stars: 76
Center Coordinate of grid cell 3 are (256, 1280) --- Number of PSF stars: 69
Center Coordinate of grid cell 4 are (256, 1792) --- Number of PSF stars: 90
Center Coordinate of grid cell 5 are (768, 256) --- Number of PSF stars: 86
Center Coordinate of grid cell 6 are (768, 768) --- Number of PSF stars: 76
Center Coordinate of grid cell 7 are (768, 1280) --- Number of PSF stars: 84
Center Coordinate of grid cell 8 are (768, 1792) --- Number of PSF stars: 83
Center Coordinate of grid cell 9 are (1280, 256) --- Number of PSF stars: 79
Center Coordinate of grid cell 10 are (1280, 768) --- Number of PSF stars: 89
Center Coordinate of grid cell 11 are (1280, 1280) --- Number of PSF stars: 63
Center Coordinate of grid cell 12 are (1280, 1792) --- Number of PSF stars: 71
Center Coordinate of grid cell 13 are (1792, 256) --- Number of PSF stars: 72
Center Coordinate of grid cell 14 are (1792, 768) --- Number of PSF stars: 67
Center Coordinate of grid cell 15 are (1792, 1280) --- Number of PSF stars: 71
Center Coordinate of grid cell 16 are (1792, 1792) --- Number of PSF stars: 70

Calculating the number of PSF stars in a 3 x 3 grid:

Center Coordinate of grid cell 1 are (341, 341) --- Number of PSF stars: 188
Center Coordinate of grid cell 2 are (341, 1023) --- Number of PSF stars: 131
Center Coordinate of grid cell 3 are (341, 1705) --- Number of PSF stars: 155
Center Coordinate of grid cell 4 are (1023, 341) --- Number of PSF stars: 138
Center Coordinate of grid cell 5 are (1023, 1023) --- Number of PSF stars: 145
Center Coordinate of grid cell 6 are (1023, 1705) --- Number of PSF stars: 132
Center Coordinate of grid cell 7 are (1705, 341) --- Number of PSF stars: 134
Center Coordinate of grid cell 8 are (1705, 1023) --- Number of PSF stars: 121
Center Coordinate of grid cell 9 are (1705, 1705) --- Number of PSF stars: 124

Calculating the number of PSF stars in a 2 x 2 grid:

Center Coordinate of grid cell 1 are (512, 512) --- Number of PSF stars: 366
Center Coordinate of grid cell 2 are (512, 1536) --- Number of PSF stars: 333
Center Coordinate of grid cell 3 are (1536, 512) --- Number of PSF stars: 312
Center Coordinate of grid cell 4 are (1536, 1536) --- Number of PSF stars: 281

Analyzing image 1 of filter F200W, detector NRCB1 

Calculating the number of PSF stars in a 5 x 5 grid:

Center Coordinate of grid cell 1 are (204, 204) --- Number of PSF stars: 81
Center Coordinate of grid cell 2 are (204, 612) --- Number of PSF stars: 49
Center Coordinate of grid cell 3 are (204, 1020) --- Number of PSF stars: 45
Center Coordinate of grid cell 4 are (204, 1428) --- Number of PSF stars: 46
Center Coordinate of grid cell 5 are (204, 1836) --- Number of PSF stars: 46
Center Coordinate of grid cell 6 are (612, 204) --- Number of PSF stars: 65
Center Coordinate of grid cell 7 are (612, 612) --- Number of PSF stars: 46
Center Coordinate of grid cell 8 are (612, 1020) --- Number of PSF stars: 43
Center Coordinates of grid cell 1 are (612, 1428) --- Not enough PSF stars in the cell (number of PSF stars < 40)
Center Coordinate of grid cell 10 are (612, 1836) --- Number of PSF stars: 57
Center Coordinate of grid cell 11 are (1020, 204) --- Number of PSF stars: 43
Center Coordinate of grid cell 12 are (1020, 612) --- Number of PSF stars: 47
Center Coordinate of grid cell 13 are (1020, 1020) --- Number of PSF stars: 55
Center Coordinates of grid cell 1 are (1020, 1428) --- Not enough PSF stars in the cell (number of PSF stars < 40)
Center Coordinates of grid cell 1 are (1020, 1836) --- Not enough PSF stars in the cell (number of PSF stars < 40)
Center Coordinate of grid cell 16 are (1428, 204) --- Number of PSF stars: 46
Center Coordinate of grid cell 17 are (1428, 612) --- Number of PSF stars: 41
Center Coordinate of grid cell 18 are (1428, 1020) --- Number of PSF stars: 41
Center Coordinate of grid cell 19 are (1428, 1428) --- Number of PSF stars: 46
Center Coordinate of grid cell 20 are (1428, 1836) --- Number of PSF stars: 57
Center Coordinate of grid cell 21 are (1836, 204) --- Number of PSF stars: 43
Center Coordinate of grid cell 22 are (1836, 612) --- Number of PSF stars: 44
Center Coordinate of grid cell 23 are (1836, 1020) --- Number of PSF stars: 49
Center Coordinate of grid cell 24 are (1836, 1428) --- Number of PSF stars: 42
Center Coordinates of grid cell 1 are (1836, 1836) --- Not enough PSF stars in the cell (number of PSF stars < 40)

Calculating the number of PSF stars in a 4 x 4 grid:

Center Coordinate of grid cell 1 are (256, 256) --- Number of PSF stars: 149
Center Coordinate of grid cell 2 are (256, 768) --- Number of PSF stars: 79
Center Coordinate of grid cell 3 are (256, 1280) --- Number of PSF stars: 65
Center Coordinate of grid cell 4 are (256, 1792) --- Number of PSF stars: 81
Center Coordinate of grid cell 5 are (768, 256) --- Number of PSF stars: 75
Center Coordinate of grid cell 6 are (768, 768) --- Number of PSF stars: 68
Center Coordinate of grid cell 7 are (768, 1280) --- Number of PSF stars: 80
Center Coordinate of grid cell 8 are (768, 1792) --- Number of PSF stars: 77
Center Coordinate of grid cell 9 are (1280, 256) --- Number of PSF stars: 71
Center Coordinate of grid cell 10 are (1280, 768) --- Number of PSF stars: 89
Center Coordinate of grid cell 11 are (1280, 1280) --- Number of PSF stars: 57
Center Coordinate of grid cell 12 are (1280, 1792) --- Number of PSF stars: 71
Center Coordinate of grid cell 13 are (1792, 256) --- Number of PSF stars: 72
Center Coordinate of grid cell 14 are (1792, 768) --- Number of PSF stars: 56
Center Coordinate of grid cell 15 are (1792, 1280) --- Number of PSF stars: 70
Center Coordinate of grid cell 16 are (1792, 1792) --- Number of PSF stars: 69

Calculating the number of PSF stars in a 3 x 3 grid:

Center Coordinate of grid cell 1 are (341, 341) --- Number of PSF stars: 211
Center Coordinate of grid cell 2 are (341, 1023) --- Number of PSF stars: 121
Center Coordinate of grid cell 3 are (341, 1705) --- Number of PSF stars: 142
Center Coordinate of grid cell 4 are (1023, 341) --- Number of PSF stars: 127
Center Coordinate of grid cell 5 are (1023, 1023) --- Number of PSF stars: 145
Center Coordinate of grid cell 6 are (1023, 1705) --- Number of PSF stars: 123
Center Coordinate of grid cell 7 are (1705, 341) --- Number of PSF stars: 123
Center Coordinate of grid cell 8 are (1705, 1023) --- Number of PSF stars: 107
Center Coordinate of grid cell 9 are (1705, 1705) --- Number of PSF stars: 128

Calculating the number of PSF stars in a 2 x 2 grid:

Center Coordinate of grid cell 1 are (512, 512) --- Number of PSF stars: 376
Center Coordinate of grid cell 2 are (512, 1536) --- Number of PSF stars: 308
Center Coordinate of grid cell 3 are (1536, 512) --- Number of PSF stars: 291
Center Coordinate of grid cell 4 are (1536, 1536) --- Number of PSF stars: 277

Analyzing image 2 of filter F200W, detector NRCB1 

Calculating the number of PSF stars in a 5 x 5 grid:

Center Coordinate of grid cell 1 are (204, 204) --- Number of PSF stars: 78
Center Coordinate of grid cell 2 are (204, 612) --- Number of PSF stars: 48
Center Coordinate of grid cell 3 are (204, 1020) --- Number of PSF stars: 45
Center Coordinate of grid cell 4 are (204, 1428) --- Number of PSF stars: 40
Center Coordinate of grid cell 5 are (204, 1836) --- Number of PSF stars: 47
Center Coordinate of grid cell 6 are (612, 204) --- Number of PSF stars: 77
Center Coordinate of grid cell 7 are (612, 612) --- Number of PSF stars: 45
Center Coordinates of grid cell 2 are (612, 1020) --- Not enough PSF stars in the cell (number of PSF stars < 40)
Center Coordinate of grid cell 9 are (612, 1428) --- Number of PSF stars: 43
Center Coordinate of grid cell 10 are (612, 1836) --- Number of PSF stars: 60
Center Coordinate of grid cell 11 are (1020, 204) --- Number of PSF stars: 47
Center Coordinate of grid cell 12 are (1020, 612) --- Number of PSF stars: 48
Center Coordinate of grid cell 13 are (1020, 1020) --- Number of PSF stars: 56
Center Coordinates of grid cell 2 are (1020, 1428) --- Not enough PSF stars in the cell (number of PSF stars < 40)
Center Coordinates of grid cell 2 are (1020, 1836) --- Not enough PSF stars in the cell (number of PSF stars < 40)
Center Coordinate of grid cell 16 are (1428, 204) --- Number of PSF stars: 49
Center Coordinate of grid cell 17 are (1428, 612) --- Number of PSF stars: 42
Center Coordinate of grid cell 18 are (1428, 1020) --- Number of PSF stars: 42
Center Coordinate of grid cell 19 are (1428, 1428) --- Number of PSF stars: 47
Center Coordinate of grid cell 20 are (1428, 1836) --- Number of PSF stars: 51
Center Coordinate of grid cell 21 are (1836, 204) --- Number of PSF stars: 40
Center Coordinate of grid cell 22 are (1836, 612) --- Number of PSF stars: 47
Center Coordinate of grid cell 23 are (1836, 1020) --- Number of PSF stars: 53
Center Coordinate of grid cell 24 are (1836, 1428) --- Number of PSF stars: 41
Center Coordinates of grid cell 2 are (1836, 1836) --- Not enough PSF stars in the cell (number of PSF stars < 40)

Calculating the number of PSF stars in a 4 x 4 grid:

Center Coordinate of grid cell 1 are (256, 256) --- Number of PSF stars: 155
Center Coordinate of grid cell 2 are (256, 768) --- Number of PSF stars: 77
Center Coordinate of grid cell 3 are (256, 1280) --- Number of PSF stars: 63
Center Coordinate of grid cell 4 are (256, 1792) --- Number of PSF stars: 80
Center Coordinate of grid cell 5 are (768, 256) --- Number of PSF stars: 80
Center Coordinate of grid cell 6 are (768, 768) --- Number of PSF stars: 69
Center Coordinate of grid cell 7 are (768, 1280) --- Number of PSF stars: 81
Center Coordinate of grid cell 8 are (768, 1792) --- Number of PSF stars: 80
Center Coordinate of grid cell 9 are (1280, 256) --- Number of PSF stars: 77
Center Coordinate of grid cell 10 are (1280, 768) --- Number of PSF stars: 91
Center Coordinate of grid cell 11 are (1280, 1280) --- Number of PSF stars: 57
Center Coordinate of grid cell 12 are (1280, 1792) --- Number of PSF stars: 67
Center Coordinate of grid cell 13 are (1792, 256) --- Number of PSF stars: 68
Center Coordinate of grid cell 14 are (1792, 768) --- Number of PSF stars: 58
Center Coordinate of grid cell 15 are (1792, 1280) --- Number of PSF stars: 66
Center Coordinate of grid cell 16 are (1792, 1792) --- Number of PSF stars: 67

Calculating the number of PSF stars in a 3 x 3 grid:

Center Coordinate of grid cell 1 are (341, 341) --- Number of PSF stars: 216
Center Coordinate of grid cell 2 are (341, 1023) --- Number of PSF stars: 119
Center Coordinate of grid cell 3 are (341, 1705) --- Number of PSF stars: 142
Center Coordinate of grid cell 4 are (1023, 341) --- Number of PSF stars: 132
Center Coordinate of grid cell 5 are (1023, 1023) --- Number of PSF stars: 147
Center Coordinate of grid cell 6 are (1023, 1705) --- Number of PSF stars: 124
Center Coordinate of grid cell 7 are (1705, 341) --- Number of PSF stars: 124
Center Coordinate of grid cell 8 are (1705, 1023) --- Number of PSF stars: 110
Center Coordinate of grid cell 9 are (1705, 1705) --- Number of PSF stars: 121

Calculating the number of PSF stars in a 2 x 2 grid:

Center Coordinate of grid cell 1 are (512, 512) --- Number of PSF stars: 385
Center Coordinate of grid cell 2 are (512, 1536) --- Number of PSF stars: 309
Center Coordinate of grid cell 3 are (1536, 512) --- Number of PSF stars: 301
Center Coordinate of grid cell 4 are (1536, 1536) --- Number of PSF stars: 263

Analyzing image 3 of filter F200W, detector NRCB1 

Calculating the number of PSF stars in a 5 x 5 grid:

Center Coordinate of grid cell 1 are (204, 204) --- Number of PSF stars: 76
Center Coordinate of grid cell 2 are (204, 612) --- Number of PSF stars: 52
Center Coordinate of grid cell 3 are (204, 1020) --- Number of PSF stars: 41
Center Coordinate of grid cell 4 are (204, 1428) --- Number of PSF stars: 45
Center Coordinate of grid cell 5 are (204, 1836) --- Number of PSF stars: 46
Center Coordinate of grid cell 6 are (612, 204) --- Number of PSF stars: 69
Center Coordinate of grid cell 7 are (612, 612) --- Number of PSF stars: 43
Center Coordinate of grid cell 8 are (612, 1020) --- Number of PSF stars: 44
Center Coordinate of grid cell 9 are (612, 1428) --- Number of PSF stars: 45
Center Coordinate of grid cell 10 are (612, 1836) --- Number of PSF stars: 55
Center Coordinate of grid cell 11 are (1020, 204) --- Number of PSF stars: 46
Center Coordinate of grid cell 12 are (1020, 612) --- Number of PSF stars: 51
Center Coordinate of grid cell 13 are (1020, 1020) --- Number of PSF stars: 54
Center Coordinates of grid cell 3 are (1020, 1428) --- Not enough PSF stars in the cell (number of PSF stars < 40)
Center Coordinates of grid cell 3 are (1020, 1836) --- Not enough PSF stars in the cell (number of PSF stars < 40)
Center Coordinate of grid cell 16 are (1428, 204) --- Number of PSF stars: 46
Center Coordinates of grid cell 3 are (1428, 612) --- Not enough PSF stars in the cell (number of PSF stars < 40)
Center Coordinate of grid cell 18 are (1428, 1020) --- Number of PSF stars: 42
Center Coordinate of grid cell 19 are (1428, 1428) --- Number of PSF stars: 46
Center Coordinate of grid cell 20 are (1428, 1836) --- Number of PSF stars: 55
Center Coordinate of grid cell 21 are (1836, 204) --- Number of PSF stars: 43
Center Coordinate of grid cell 22 are (1836, 612) --- Number of PSF stars: 45
Center Coordinate of grid cell 23 are (1836, 1020) --- Number of PSF stars: 51
Center Coordinate of grid cell 24 are (1836, 1428) --- Number of PSF stars: 43
Center Coordinates of grid cell 3 are (1836, 1836) --- Not enough PSF stars in the cell (number of PSF stars < 40)

Calculating the number of PSF stars in a 4 x 4 grid:

Center Coordinate of grid cell 1 are (256, 256) --- Number of PSF stars: 156
Center Coordinate of grid cell 2 are (256, 768) --- Number of PSF stars: 76
Center Coordinate of grid cell 3 are (256, 1280) --- Number of PSF stars: 66
Center Coordinate of grid cell 4 are (256, 1792) --- Number of PSF stars: 79
Center Coordinate of grid cell 5 are (768, 256) --- Number of PSF stars: 76
Center Coordinate of grid cell 6 are (768, 768) --- Number of PSF stars: 69
Center Coordinate of grid cell 7 are (768, 1280) --- Number of PSF stars: 82
Center Coordinate of grid cell 8 are (768, 1792) --- Number of PSF stars: 74
Center Coordinate of grid cell 9 are (1280, 256) --- Number of PSF stars: 71
Center Coordinate of grid cell 10 are (1280, 768) --- Number of PSF stars: 88
Center Coordinate of grid cell 11 are (1280, 1280) --- Number of PSF stars: 57
Center Coordinate of grid cell 12 are (1280, 1792) --- Number of PSF stars: 70
Center Coordinate of grid cell 13 are (1792, 256) --- Number of PSF stars: 70
Center Coordinate of grid cell 14 are (1792, 768) --- Number of PSF stars: 61
Center Coordinate of grid cell 15 are (1792, 1280) --- Number of PSF stars: 69
Center Coordinate of grid cell 16 are (1792, 1792) --- Number of PSF stars: 71

Calculating the number of PSF stars in a 3 x 3 grid:

Center Coordinate of grid cell 1 are (341, 341) --- Number of PSF stars: 218
Center Coordinate of grid cell 2 are (341, 1023) --- Number of PSF stars: 115
Center Coordinate of grid cell 3 are (341, 1705) --- Number of PSF stars: 145
Center Coordinate of grid cell 4 are (1023, 341) --- Number of PSF stars: 131
Center Coordinate of grid cell 5 are (1023, 1023) --- Number of PSF stars: 146
Center Coordinate of grid cell 6 are (1023, 1705) --- Number of PSF stars: 123
Center Coordinate of grid cell 7 are (1705, 341) --- Number of PSF stars: 124
Center Coordinate of grid cell 8 are (1705, 1023) --- Number of PSF stars: 113
Center Coordinate of grid cell 9 are (1705, 1705) --- Number of PSF stars: 127

Calculating the number of PSF stars in a 2 x 2 grid:

Center Coordinate of grid cell 1 are (512, 512) --- Number of PSF stars: 383
Center Coordinate of grid cell 2 are (512, 1536) --- Number of PSF stars: 305
Center Coordinate of grid cell 3 are (1536, 512) --- Number of PSF stars: 294
Center Coordinate of grid cell 4 are (1536, 1536) --- Number of PSF stars: 275

Analyzing image 4 of filter F200W, detector NRCB1 

Calculating the number of PSF stars in a 5 x 5 grid:

Center Coordinate of grid cell 1 are (204, 204) --- Number of PSF stars: 73
Center Coordinate of grid cell 2 are (204, 612) --- Number of PSF stars: 47
Center Coordinate of grid cell 3 are (204, 1020) --- Number of PSF stars: 42
Center Coordinate of grid cell 4 are (204, 1428) --- Number of PSF stars: 46
Center Coordinate of grid cell 5 are (204, 1836) --- Number of PSF stars: 49
Center Coordinate of grid cell 6 are (612, 204) --- Number of PSF stars: 73
Center Coordinate of grid cell 7 are (612, 612) --- Number of PSF stars: 45
Center Coordinate of grid cell 8 are (612, 1020) --- Number of PSF stars: 42
Center Coordinate of grid cell 9 are (612, 1428) --- Number of PSF stars: 41
Center Coordinate of grid cell 10 are (612, 1836) --- Number of PSF stars: 57
Center Coordinate of grid cell 11 are (1020, 204) --- Number of PSF stars: 45
Center Coordinate of grid cell 12 are (1020, 612) --- Number of PSF stars: 45
Center Coordinate of grid cell 13 are (1020, 1020) --- Number of PSF stars: 54
Center Coordinates of grid cell 4 are (1020, 1428) --- Not enough PSF stars in the cell (number of PSF stars < 40)
Center Coordinates of grid cell 4 are (1020, 1836) --- Not enough PSF stars in the cell (number of PSF stars < 40)
Center Coordinate of grid cell 16 are (1428, 204) --- Number of PSF stars: 52
Center Coordinate of grid cell 17 are (1428, 612) --- Number of PSF stars: 41
Center Coordinate of grid cell 18 are (1428, 1020) --- Number of PSF stars: 45
Center Coordinate of grid cell 19 are (1428, 1428) --- Number of PSF stars: 46
Center Coordinate of grid cell 20 are (1428, 1836) --- Number of PSF stars: 51
Center Coordinates of grid cell 4 are (1836, 204) --- Not enough PSF stars in the cell (number of PSF stars < 40)
Center Coordinate of grid cell 22 are (1836, 612) --- Number of PSF stars: 49
Center Coordinate of grid cell 23 are (1836, 1020) --- Number of PSF stars: 55
Center Coordinate of grid cell 24 are (1836, 1428) --- Number of PSF stars: 42
Center Coordinates of grid cell 4 are (1836, 1836) --- Not enough PSF stars in the cell (number of PSF stars < 40)

Calculating the number of PSF stars in a 4 x 4 grid:

Center Coordinate of grid cell 1 are (256, 256) --- Number of PSF stars: 143
Center Coordinate of grid cell 2 are (256, 768) --- Number of PSF stars: 75
Center Coordinate of grid cell 3 are (256, 1280) --- Number of PSF stars: 64
Center Coordinate of grid cell 4 are (256, 1792) --- Number of PSF stars: 85
Center Coordinate of grid cell 5 are (768, 256) --- Number of PSF stars: 78
Center Coordinate of grid cell 6 are (768, 768) --- Number of PSF stars: 69
Center Coordinate of grid cell 7 are (768, 1280) --- Number of PSF stars: 80
Center Coordinate of grid cell 8 are (768, 1792) --- Number of PSF stars: 75
Center Coordinate of grid cell 9 are (1280, 256) --- Number of PSF stars: 76
Center Coordinate of grid cell 10 are (1280, 768) --- Number of PSF stars: 88
Center Coordinate of grid cell 11 are (1280, 1280) --- Number of PSF stars: 58
Center Coordinate of grid cell 12 are (1280, 1792) --- Number of PSF stars: 71
Center Coordinate of grid cell 13 are (1792, 256) --- Number of PSF stars: 68
Center Coordinate of grid cell 14 are (1792, 768) --- Number of PSF stars: 65
Center Coordinate of grid cell 15 are (1792, 1280) --- Number of PSF stars: 68
Center Coordinate of grid cell 16 are (1792, 1792) --- Number of PSF stars: 68

Calculating the number of PSF stars in a 3 x 3 grid:

Center Coordinate of grid cell 1 are (341, 341) --- Number of PSF stars: 206
Center Coordinate of grid cell 2 are (341, 1023) --- Number of PSF stars: 117
Center Coordinate of grid cell 3 are (341, 1705) --- Number of PSF stars: 147
Center Coordinate of grid cell 4 are (1023, 341) --- Number of PSF stars: 129
Center Coordinate of grid cell 5 are (1023, 1023) --- Number of PSF stars: 145
Center Coordinate of grid cell 6 are (1023, 1705) --- Number of PSF stars: 122
Center Coordinate of grid cell 7 are (1705, 341) --- Number of PSF stars: 125
Center Coordinate of grid cell 8 are (1705, 1023) --- Number of PSF stars: 116
Center Coordinate of grid cell 9 are (1705, 1705) --- Number of PSF stars: 121

Calculating the number of PSF stars in a 2 x 2 grid:

Center Coordinate of grid cell 1 are (512, 512) --- Number of PSF stars: 371
Center Coordinate of grid cell 2 are (512, 1536) --- Number of PSF stars: 310
Center Coordinate of grid cell 3 are (1536, 512) --- Number of PSF stars: 302
Center Coordinate of grid cell 4 are (1536, 1536) --- Number of PSF stars: 272

TODO - Create a grid of ePSF

Here goes the function that creates a grid of ePSF that can be saved in the epsf dictionary.

TODO - Use the ePSF grid to perturbate the WebbPSF model

Here goes the function that create a grid of PSF models obtained perturbating the WebbPSF PSF models using the ePSF grid created above.

Perform PSF photometry

We perform the PSF photometry on the images, saving by default the output catalogs and the residual images in the dictionary created below. It is also possible to save the output catalogs (pickles pandas object) and residual images (fits files) in the current directory using the parameters save_output and save_residuals.

In [23]:
dict_phot = {}

for det in dets_short:
    dict_phot.setdefault(det, {})
    for j, filt in enumerate(filts_short):
        dict_phot[det].setdefault(filt, {})

        dict_phot[det][filt]['residual images'] = {}
        dict_phot[det][filt]['output photometry tables'] = {}

        for i in np.arange(0, len(dict_images[det][filt]['images']), 1):

            dict_phot[det][filt]['residual images'][i + 1] = None
            dict_phot[det][filt]['output photometry tables'][i + 1] = None

Note: since performing the PSF photometry on the images takes some time (for the 8 images in this example ~ 4 hours), to speed up the notebook, we use a high threshold in the finding algorithm (threshold ~ 2000) and we will use in the analyis below the catalogs obtained with a sigma threshold = 10 from a previous reduction run. To perform a meaningful data reduction, the user should modify the threshold accordingly.

Here we use as PSF model the grid of WebbPSF PSFs, but the users can change the model and use the others available (i.e., single WebbPSF PSF, single ePSF) modifying the psf parameter in the function.

In [24]:
def psf_phot(det='NRCA1', filt='F070W', th=2000, psf='grid_webbpsf', save_residuals=False, save_output=False):

    bkgrms = MADStdBackgroundRMS()
    mmm_bkg = MMMBackground()
    fitter = LevMarLSQFitter()

    im = fits.open(dict_images[det][filt]['images'][i])
    imh = im[1].header
    data_sb = im[1].data

    d = im[0].header['DETECTOR']
    prim_dith_pos = im[0].header['PATT_NUM']
    prim_dith_num = im[0].header['NUMDTHPT']
    subpx_dith_pos = im[0].header['SUBPXNUM']
    subpx_dith_num = im[0].header['SUBPXPNS']

    data = data_sb / imh['PHOTMJSR']

    print("Conversion factor from {units} to DN/s for filter {f}:".format(units=imh['BUNIT'], f=filt), imh['PHOTMJSR'])
    print("Applying conversion to the data")
            
    sigma_psf = dict_utils[filt]['psf fwhm']
    print("FWHM for the filter {f}:".format(f=filt), sigma_psf)
    
    std = bkgrms(data)
    bkg = mmm_bkg(data)
    
    daofind = DAOStarFinder(threshold=th * std + bkg, fwhm=sigma_psf, roundhi=1.0, roundlo=-1.0,
                            sharplo=0.30, sharphi=1.40)
    
    daogroup = DAOGroup(5.0 * sigma_psf)
    
    # grid PSF

    if psf == 'grid_webbpsf':
        print("Using as PSF model WebbPSF PSFs grid")
        psf_model = dict_psfs_webbpsf[det][filt]['psf model grid'].copy()

    # single psf:

    if psf == 'single_webbpsf':
        print("Using as PSF model WebbPSF single PSF")
        psf_model = dict_psfs_webbpsf[det][filt]['psf model single'].copy()

    # epsf:

    if psf == 'single_epsf':
        print("Using as PSF model single ePSF")
        psf_model = dict_psfs_epsf[det][filt]['epsf single'][i + 1].copy()

    print("Performing the photometry on image {number} of filter {f}, detector {d}".format(number=i + 1, f=filt, d=det))
            
    tic = time.perf_counter()
    
    phot = IterativelySubtractedPSFPhotometry(finder=daofind, group_maker=daogroup,
                                              bkg_estimator=mmm_bkg, psf_model=psf_model,
                                              fitter=LevMarLSQFitter(),
                                              niters=2, fitshape=(11, 11), aperture_radius=ap_radius[j])
    result = phot(data)
    
    toc = time.perf_counter()
    
    print("Time needed to perform photometry on image {number}:".format(number=i + 1), "%.2f" % ((toc - tic) / 3600), "hours")
    print("Number of sources detected in image {number} for filter {f}:".format(number=i + 1, f=filt), len(result))
        
    residual_image = phot.get_residual_image()
                            
    dict_phot[det][filt]['residual images'][i + 1] = residual_image
    dict_phot[det][filt]['output photometry tables'][i + 1] = result

    # save the residual images as fits file:

    if save_residuals:
        hdu = fits.PrimaryHDU(residual_image)
        hdul = fits.HDUList([hdu])
        residual_outname = 'residual_%s_%s_webbPSF_gridPSF_%dof%d_%dof%d.fits' % (d, filt, prim_dith_pos, prim_dith_num, subpx_dith_pos, subpx_dith_num)

        dir_output_phot = './'

        hdul.writeto(os.path.join(dir_output_phot, residual_outname))

        outname = 'phot_%s_%s_webbPSF_gridPSF_level2_%dof%d_%dof%d.pkl' % (d, filt, prim_dith_pos, prim_dith_num, subpx_dith_pos, subpx_dith_num)

    # save the output photometry Tables

    if save_output:
        tab = result.to_pandas()
        tab.to_pickle(os.path.join(dir_output_phot, outname))
In [25]:
tic_tot = time.perf_counter()

ap_radius = [3.0, 3.5]  # must match the number of filters analyzed

if glob.glob('./*residual*.fits'):
    print("Deleting Residual images from directory")
    files = glob.glob('./residual*.fits')
    for file in files:
        os.remove(file)

for det in dets_short:
    for j, filt in enumerate(filts_short):
        for i in np.arange(0, len(dict_images[det][filt]['images']), 1):
            
            psf_phot(det=det, filt=filt, th=2000, psf='grid_webbpsf', save_residuals=True, save_output=False) 

toc_tot = time.perf_counter()
print("Time elapsed to perform the photometry of the {number} images:".format(number=(len(filts_short) * len(dict_images[det][filt]['images']))), "%.2f" % ((toc_tot - tic_tot) / 3600), "hours")    
Conversion factor from MJy/sr to DN/s for filter F115W: 3.821892261505127
Applying conversion to the data
FWHM for the filter F115W: 1.298
Using as PSF model WebbPSF PSFs grid
Performing the photometry on image 1 of filter F115W, detector NRCB1
WARNING: The fit may be unsuccessful; check fit_info['message'] for more information. [astropy.modeling.fitting]
WARNING: The fit may be unsuccessful; check fit_info['message'] for more information. [astropy.modeling.fitting]
Time needed to perform photometry on image 1: 0.01 hours
Number of sources detected in image 1 for filter F115W: 557
Conversion factor from MJy/sr to DN/s for filter F115W: 3.821892261505127
Applying conversion to the data
FWHM for the filter F115W: 1.298
Using as PSF model WebbPSF PSFs grid
Performing the photometry on image 2 of filter F115W, detector NRCB1
WARNING: The fit may be unsuccessful; check fit_info['message'] for more information. [astropy.modeling.fitting]
WARNING: The fit may be unsuccessful; check fit_info['message'] for more information. [astropy.modeling.fitting]
Time needed to perform photometry on image 2: 0.01 hours
Number of sources detected in image 2 for filter F115W: 567
Conversion factor from MJy/sr to DN/s for filter F115W: 3.821892261505127
Applying conversion to the data
FWHM for the filter F115W: 1.298
Using as PSF model WebbPSF PSFs grid
Performing the photometry on image 3 of filter F115W, detector NRCB1
WARNING: The fit may be unsuccessful; check fit_info['message'] for more information. [astropy.modeling.fitting]
WARNING: The fit may be unsuccessful; check fit_info['message'] for more information. [astropy.modeling.fitting]
Time needed to perform photometry on image 3: 0.01 hours
Number of sources detected in image 3 for filter F115W: 587
Conversion factor from MJy/sr to DN/s for filter F115W: 3.821892261505127
Applying conversion to the data
FWHM for the filter F115W: 1.298
Using as PSF model WebbPSF PSFs grid
Performing the photometry on image 4 of filter F115W, detector NRCB1
WARNING: The fit may be unsuccessful; check fit_info['message'] for more information. [astropy.modeling.fitting]
WARNING: The fit may be unsuccessful; check fit_info['message'] for more information. [astropy.modeling.fitting]
Time needed to perform photometry on image 4: 0.01 hours
Number of sources detected in image 4 for filter F115W: 566
Conversion factor from MJy/sr to DN/s for filter F200W: 2.564082860946655
Applying conversion to the data
FWHM for the filter F200W: 2.141
Using as PSF model WebbPSF PSFs grid
Performing the photometry on image 1 of filter F200W, detector NRCB1
WARNING: The fit may be unsuccessful; check fit_info['message'] for more information. [astropy.modeling.fitting]
WARNING: The fit may be unsuccessful; check fit_info['message'] for more information. [astropy.modeling.fitting]
Time needed to perform photometry on image 1: 0.01 hours
Number of sources detected in image 1 for filter F200W: 402
Conversion factor from MJy/sr to DN/s for filter F200W: 2.564082860946655
Applying conversion to the data
FWHM for the filter F200W: 2.141
Using as PSF model WebbPSF PSFs grid
Performing the photometry on image 2 of filter F200W, detector NRCB1
WARNING: The fit may be unsuccessful; check fit_info['message'] for more information. [astropy.modeling.fitting]
WARNING: The fit may be unsuccessful; check fit_info['message'] for more information. [astropy.modeling.fitting]
Time needed to perform photometry on image 2: 0.01 hours
Number of sources detected in image 2 for filter F200W: 428
Conversion factor from MJy/sr to DN/s for filter F200W: 2.564082860946655
Applying conversion to the data
FWHM for the filter F200W: 2.141
Using as PSF model WebbPSF PSFs grid
Performing the photometry on image 3 of filter F200W, detector NRCB1
WARNING: The fit may be unsuccessful; check fit_info['message'] for more information. [astropy.modeling.fitting]
WARNING: The fit may be unsuccessful; check fit_info['message'] for more information. [astropy.modeling.fitting]
Time needed to perform photometry on image 3: 0.01 hours
Number of sources detected in image 3 for filter F200W: 422
Conversion factor from MJy/sr to DN/s for filter F200W: 2.564082860946655
Applying conversion to the data
FWHM for the filter F200W: 2.141
Using as PSF model WebbPSF PSFs grid
Performing the photometry on image 4 of filter F200W, detector NRCB1
WARNING: The fit may be unsuccessful; check fit_info['message'] for more information. [astropy.modeling.fitting]
WARNING: The fit may be unsuccessful; check fit_info['message'] for more information. [astropy.modeling.fitting]
Time needed to perform photometry on image 4: 0.01 hours
Number of sources detected in image 4 for filter F200W: 428
Time elapsed to perform the photometry of the 8 images: 0.10 hours

Output Photometry Table

Note for developer:

It would be really useful, if PhotUtils can provide some diagnostics to identify the quality of the photometry in the final catalog for each source (similarly to all the other PSF photometry programs available).

In [26]:
dict_phot['NRCB1']['F115W']['output photometry tables'][1]
Out[26]:
Table length=557
x_0x_fity_0y_fitflux_0flux_fitidgroup_idflux_uncx_0_uncy_0_unciter_detected
float64float64float64float64float64float64int64int64float64float64float64int64
319.4246272573411319.624382960771516.69870346839162416.779812043448285501.7585915115253817.362838092945118.9902217162536590.0093071837963758330.0109313167469156651
239.07687209767755239.061795002821221.5924808475196221.389574086731667343.62489297091554557.6927464772607226.1265776866416260.0119297958020849380.0093378767748094651
486.6367667247977486.3990783962301525.29850541994612825.581923420634563604.8895459014808986.94943665790393310.831110532521940.008995137978527230.0093099454756381761
1498.5839125930491498.373208250342725.663122986048125.767128766572036378.6391370981348615.3356760509652446.7429707616086490.009071450324031240.0109864049637368961
1772.2253845727881772.157128802312925.87063262974522725.91433319889012309.433462977977502.15276048146166555.4179564353419720.0103043780709375350.0122478490659573681
1249.38898573745451249.782323777738233.1656679657410333.336732822040465405.5399073869594381.28044660161766391.03430006658440.091848775094225910.079492138464864621
1969.24956452332751969.16279636233333.2922920277642933.58300446690942867.97955655023491414.69723782299147715.1448233082391180.0101446469903817650.009128422971317131
1815.87717499671361815.90733730141439.2384644174930139.284375443138092717.0424325603573547.973719944200688100.091894308526920.031600342952483310.0259170249370553571
524.6487415112808524.40699829835947.0243260292674747.030845271298716472.5076338924839766.2118980024395998.2030520686042970.0088145364909830320.0121075644454471821
283.71266677261855283.722213784849551.7710283101328251.740368067827241290.35983041094351732.2634939523075101042.748889672126850.022529932693190670.0236407003735785471
....................................
313.86603305995726313.196825061358771514.54276730941751514.8563278296463342.1082956933899603.8680259453085332575.232177124889080.122395688017838550.141708491737891182
1594.89564627265921595.12508338025171548.81004817482311548.9063013229852472.58088565049104629.8278438885151342662.191466807589220.097132921182065370.112019008734148592
1662.15788599190071663.22654377185021707.11815299610951707.2296299090353-1152.4314388705366-801.78163495328643527220.422050016006780.248486558243293130.2463303997393032
887.2699332861704887.30104833576641771.82727707107691771.814971784264414.8638966898997689.2459645066103362850.9410447363914760.065380702605029260.082499221695502752
632.1139545087674632.24133615890651832.1319039612621833.319054508187-1209.7248563610865-869.43227280764293729192.427159948266930.204754132360664480.18833779531474122
1953.56072785383051953.70493015589981878.20669053450111878.613283434624373.2331683449073669.9881881867456383081.117729831680390.12519783998315550.117959754230248682
81.0089160067475880.811348511323271946.9526143380151948.278489889208-1219.1934508690745-919.99094764073663931184.346943945624160.196860418888685020.173351983586563542
220.02187625848927220.28366836014791946.97408848238271948.2627148391628-904.5799652848325-679.97293078318984032137.338325853735850.18384401556273710.17554838603553232
904.6863753881605904.19338251555671981.6652710542731981.8892865120495399.6678295172721615.9851749936068413367.323966104319620.104273083336648960.122678888766636322
1914.84936393768861914.39522079368822037.96556245756532037.2523008520304785.5765981027541430.70312708378364234294.077340756025540.17876399672461270.184907245306359882

Display subtracted image

As an example, we show the comparison between one science image and the residual image after the data reduction for both filters. Note that the residual image is obtained from the photometry run in the cell above with a very high detection threshold.

In [27]:
plt.figure(figsize=(14, 14))

for det in dets_short:
    for i, filt in enumerate(filts_short):

        image = fits.open(dict_images[det][filt]['images'][0])
        data_sb = image[1].data

        ax = plt.subplot(2, len(filts_short), i + 1)

        plt.xlabel("X [px]", fontdict=font2)
        plt.ylabel("Y [px]", fontdict=font2)
        plt.title(filt, fontdict=font2)
        norm = simple_norm(data_sb, 'sqrt', percent=99.)

        ax.imshow(data_sb, norm=norm, cmap='Greys')

for det in dets_short:
    for i, filt in enumerate(filts_short):

        res = dict_phot[det][filt]['residual images'][1]

        ax = plt.subplot(2, len(filts_short), i + 3)

        plt.xlabel("X [px]", fontdict=font2)
        plt.ylabel("Y [px]", fontdict=font2)
        norm = simple_norm(data_sb, 'sqrt', percent=99.)

        ax.imshow(res, norm=norm, cmap='Greys')

plt.tight_layout()

Part II - Data Analysis

Note: here we use the reduction obtained using a grid of WebbPSF PSFs as PSF models. The users can perform the data analysis using different PSF models (single PSF model, PSF grid, etc.) and compare the results.

Load Tables with PSF Photometry

In [28]:
if not glob.glob('./*phot*gridPSF*.pkl'):

    print("Downloading Photometry Output")

    boxlink_cat_f115w = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/stellar_photometry/phot_cat_F115W.tar.gz'
    boxfile_cat_f115w = './phot_cat_F115W.tar.gz'
    urllib.request.urlretrieve(boxlink_cat_f115w, boxfile_cat_f115w)

    tar = tarfile.open(boxfile_cat_f115w, 'r')
    tar.extractall()

    boxlink_cat_f200w = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/stellar_photometry/phot_cat_F200W.tar.gz'
    boxfile_cat_f200w = './phot_cat_F200W.tar.gz'
    urllib.request.urlretrieve(boxlink_cat_f200w, boxfile_cat_f200w)

    tar = tarfile.open(boxfile_cat_f200w, 'r')
    tar.extractall()

    cat_dir = './'
    phots_pkl_f115w = sorted(glob.glob(os.path.join(cat_dir, '*F115W*gridPSF*.pkl')))
    phots_pkl_f200w = sorted(glob.glob(os.path.join(cat_dir, '*F200W*gridPSF*.pkl')))                       

else:

    cat_dir = './'
    phots_pkl_f115w = sorted(glob.glob(os.path.join(cat_dir, '*F115W*gridPSF*.pkl')))
    phots_pkl_f200w = sorted(glob.glob(os.path.join(cat_dir, '*F200W*gridPSF*.pkl')))                      

results_f115w = []
results_f200w = []

for phot_pkl_f115w, phot_pkl_f200w in zip(phots_pkl_f115w, phots_pkl_f200w):

    ph_f115w = pd.read_pickle(phot_pkl_f115w)
    ph_f200w = pd.read_pickle(phot_pkl_f200w)

    result_f115w = QTable.from_pandas(ph_f115w)
    result_f200w = QTable.from_pandas(ph_f200w)

    results_f115w.append(result_f115w)
    results_f200w.append(result_f200w)
Downloading Photometry Output

Transform the images to DataModel

In order to assign the WCS coordinate and hence cross-match the images, we need to transform the images to DataModel. The coordinates are assigned during the step assign_wcs step in the JWST pipeline and allow us to cross-match the different catalogs obtained for each filter.

In [29]:
images_f115w = []
images_f200w = []

for i in np.arange(0, len(dict_images['NRCB1']['F115W']['images']), 1):

    image_f115w = ImageModel(dict_images['NRCB1']['F115W']['images'][i])
    images_f115w.append(image_f115w)
        
for i in np.arange(0, len(dict_images['NRCB1']['F200W']['images']), 1):

    image_f200w = ImageModel(dict_images['NRCB1']['F200W']['images'][i])
    images_f200w.append(image_f200w)

Cross-match the catalogs from the two filters for the 4 images

We cross-match the catalogs to obtain the single color-magnitude diagrams.

Stars from the two filters are associated if the distance between the matches is < 0.5 px.

In [30]:
results_clean_f115w = []
results_clean_f200w = []

for i in np.arange(0, len(images_f115w), 1):

    mask_f115w = ((results_f115w[i]['x_fit'] > 0) & (results_f115w[i]['x_fit'] < 2048) &
                  (results_f115w[i]['y_fit'] > 0) & (results_f115w[i]['y_fit'] < 2048) &
                  (results_f115w[i]['flux_fit'] > 0))

    result_clean_f115w = results_f115w[i][mask_f115w]

    ra_f115w, dec_f115w = images_f115w[i].meta.wcs(result_clean_f115w['x_fit'], result_clean_f115w['y_fit'])
    radec_f115w = SkyCoord(ra_f115w, dec_f115w, unit='deg')
    result_clean_f115w['radec'] = radec_f115w
    results_clean_f115w.append(result_clean_f115w)

    mask_f200w = ((results_f200w[i]['x_fit'] > 0) & (results_f200w[i]['x_fit'] < 2048) &
                  (results_f200w[i]['y_fit'] > 0) & (results_f200w[i]['y_fit'] < 2048) &
                  (results_f200w[i]['flux_fit'] > 0))

    result_clean_f200w = results_f200w[i][mask_f200w]

    ra_f200w, dec_f200w = images_f200w[i].meta.wcs(result_clean_f200w['x_fit'], result_clean_f200w['y_fit'])
    radec_f200w = SkyCoord(ra_f200w, dec_f200w, unit='deg')

    result_clean_f200w['radec'] = radec_f200w
    results_clean_f200w.append(result_clean_f200w)
In [31]:
max_sep = 0.015 * u.arcsec

matches_phot_single = []
filt1 = 'F115W'
filt2 = 'F200W'

for res1, res2 in zip(results_clean_f115w, results_clean_f200w):

    idx, d2d, _ = match_coordinates_sky(res1['radec'], res2['radec'])

    sep_constraint = d2d < max_sep

    match_phot_single = Table()

    x_0_f115w = res1['x_0'][sep_constraint]
    y_0_f115w = res1['y_0'][sep_constraint]
    x_fit_f115w = res1['x_fit'][sep_constraint]
    y_fit_f115w = res1['y_fit'][sep_constraint]
    radec_f115w = res1['radec'][sep_constraint]
    mag_f115w = (-2.5 * np.log10(res1['flux_fit']))[sep_constraint]
    emag_f115w = (1.086 * (res1['flux_unc'] / res1['flux_fit']))[sep_constraint]

    x_0_f200w = res2['x_0'][idx[sep_constraint]]
    y_0_f200w = res2['y_0'][idx[sep_constraint]]
    x_fit_f200w = res2['x_fit'][idx[sep_constraint]]
    y_fit_f200w = res2['y_fit'][idx[sep_constraint]]
    radec_f200w = res2['radec'][idx][sep_constraint]
    mag_f200w = (-2.5 * np.log10(res2['flux_fit']))[idx[sep_constraint]]
    emag_f200w = (1.086 * (res2['flux_unc'] / res2['flux_fit']))[idx[sep_constraint]]

    match_phot_single['x_0_' + filt1] = x_0_f115w
    match_phot_single['y_0_' + filt1] = y_0_f115w
    match_phot_single['x_fit_' + filt1] = x_fit_f115w
    match_phot_single['y_fit_' + filt1] = y_fit_f115w
    match_phot_single['radec_' + filt1] = radec_f115w
    match_phot_single['mag_' + filt1] = mag_f115w
    match_phot_single['emag_' + filt1] = emag_f115w
    match_phot_single['x_0_' + filt2] = x_0_f200w
    match_phot_single['y_0_' + filt2] = y_0_f200w
    match_phot_single['x_fit_' + filt2] = x_fit_f200w
    match_phot_single['y_fit_' + filt2] = y_fit_f200w
    match_phot_single['radec_' + filt2] = radec_f200w
    match_phot_single['mag_' + filt2] = mag_f200w
    match_phot_single['emag_' + filt2] = emag_f200w

    matches_phot_single.append(match_phot_single)    

Color-Magnitude Diagrams (Instrumental Magnitudes) for the 4 images

In [32]:
plt.figure(figsize=(12, 16))
plt.clf()

for i in np.arange(0, len(matches_phot_single), 1):
    ax = plt.subplot(2, 2, i + 1)

    j = str(i + 1)

    xlim0 = -0.5
    xlim1 = 0.8
    ylim0 = -1
    ylim1 = -9

    ax.set_xlim(xlim0, xlim1)
    ax.set_ylim(ylim0, ylim1)

    ax.xaxis.set_major_locator(ticker.AutoLocator())
    ax.xaxis.set_minor_locator(ticker.AutoMinorLocator())
    ax.yaxis.set_major_locator(ticker.AutoLocator())
    ax.yaxis.set_minor_locator(ticker.AutoMinorLocator())

    f115w_single = matches_phot_single[i]['mag_' + filt1]
    f200w_single = matches_phot_single[i]['mag_' + filt2]

    ax.scatter(f115w_single - f200w_single, f115w_single, s=1, color='k')

    ax.set_xlabel(filt1 + '-' + filt2, fontdict=font2)
    ax.set_ylabel(filt1, fontdict=font2)
    ax.text(xlim0 + 0.1, -8.65, "Image %s" % j, fontdict=font2)
    
plt.tight_layout()

Difference in retrieved positions (in pixels) between daofind an PSF routine

We show the difference in the stars position derived from daofind and the psf fitting algorithm. We also show the difference $\Delta$X and $\Delta$Y as a function of the instrumental magnitudes.

In [33]:
plt.figure(figsize=(12, 6))

ax1 = plt.subplot(1, 2, 1)

xlim0 = -1
xlim1 = 1
ylim0 = -1
ylim1 = 1

ax1.set_xlim(xlim0, xlim1)
ax1.set_ylim(ylim0, ylim1)

ax1.xaxis.set_major_locator(ticker.AutoLocator())
ax1.xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax1.yaxis.set_major_locator(ticker.AutoLocator())
ax1.yaxis.set_minor_locator(ticker.AutoMinorLocator())

x_find_f115w = results_clean_f115w[0]['x_0']
y_find_f115w = results_clean_f115w[0]['y_0']

x_psf_f115w = results_clean_f115w[0]['x_fit']
y_psf_f115w = results_clean_f115w[0]['y_fit']

delta_x_f115w = x_find_f115w - x_psf_f115w
delta_y_f115w = y_find_f115w - y_psf_f115w

_, d_x_f115w, sigma_d_x_f115w = sigma_clipped_stats(delta_x_f115w)
_, d_y_f115w, sigma_d_y_f115w = sigma_clipped_stats(delta_y_f115w)

ax1.scatter(delta_x_f115w, delta_y_f115w, s=1, color='gray')

ax1.set_xlabel('$\Delta$ X (px)', fontdict=font2)
ax1.set_ylabel('$\Delta$ Y (px)', fontdict=font2)
ax1.set_title(filt1, fontdict=font2)
ax1.text(xlim0 + 0.05, ylim1 - 0.15, ' $\Delta$ X = %5.3f $\pm$ %5.3f' % (d_x_f115w, sigma_d_x_f115w),
         color='k', fontdict=font2)
ax1.text(xlim0 + 0.05, ylim1 - 0.30, ' $\Delta$ Y = %5.3f $\pm$ %5.3f' % (d_y_f115w, sigma_d_y_f115w),
         color='k', fontdict=font2)
ax1.plot([0, 0], [ylim0, ylim1], color='k', lw=2, ls='--')
ax1.plot([xlim0, xlim1], [0, 0], color='k', lw=2, ls='--')

ax2 = plt.subplot(1, 2, 2)

ax2.set_xlim(xlim0, xlim1)
ax2.set_ylim(ylim0, ylim1)

ax2.xaxis.set_major_locator(ticker.AutoLocator())
ax2.xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax2.yaxis.set_major_locator(ticker.AutoLocator())
ax2.yaxis.set_minor_locator(ticker.AutoMinorLocator())

x_find_f200w = results_clean_f200w[0]['x_0']
y_find_f200w = results_clean_f200w[0]['y_0']

x_psf_f200w = results_clean_f200w[0]['x_fit']
y_psf_f200w = results_clean_f200w[0]['y_fit']

delta_x_f200w = x_find_f200w - x_psf_f200w
delta_y_f200w = y_find_f200w - y_psf_f200w

_, d_x_f200w, sigma_d_x_f200w = sigma_clipped_stats(delta_x_f200w)
_, d_y_f200w, sigma_d_y_f200w = sigma_clipped_stats(delta_y_f200w)

ax2.scatter(delta_x_f200w, delta_y_f200w, s=1, color='gray')
ax2.text(xlim0 + 0.05, ylim1 - 0.15, ' $\Delta$ X = %5.3f $\pm$ %5.3f' % (d_x_f200w, sigma_d_x_f200w),
         color='k', fontdict=font2)
ax2.text(xlim0 + 0.05, ylim1 - 0.30, ' $\Delta$ Y = %5.3f $\pm$ %5.3f' % (d_y_f200w, sigma_d_y_f200w),
         color='k', fontdict=font2)
ax2.plot([0, 0], [ylim0, ylim1], color='k', lw=2, ls='--')
ax2.plot([xlim0, xlim1], [0, 0], color='k', lw=2, ls='--')

ax2.set_xlabel('$\Delta$ X (px)', fontdict=font2)
ax2.set_ylabel('$\Delta$ Y (px)', fontdict=font2)
ax2.set_title(filt2, fontdict=font2)

plt.tight_layout()
In [34]:
plt.figure(figsize=(12, 8))

ax1 = plt.subplot(2, 2, 1)

xlim0 = -9
xlim1 = -1
ylim0 = -1
ylim1 = 1

ax1.set_xlim(xlim0, xlim1)
ax1.set_ylim(ylim0, ylim1)

ax1.xaxis.set_major_locator(ticker.AutoLocator())
ax1.xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax1.yaxis.set_major_locator(ticker.AutoLocator())
ax1.yaxis.set_minor_locator(ticker.AutoMinorLocator())

mag_inst_f115w = -2.5 * np.log10(results_clean_f115w[0]['flux_fit'])

ax1.scatter(mag_inst_f115w, delta_x_f115w, s=1, color='gray')
ax1.plot([xlim0, xlim1], [0, 0], color='k', lw=2, ls='--')

ax1.set_xlabel(filt1 + '_inst', fontdict=font2)
ax1.set_ylabel('$\Delta$ X (px)', fontdict=font2)

ax2 = plt.subplot(2, 2, 2)

ax2.set_xlim(xlim0, xlim1)
ax2.set_ylim(ylim0, ylim1)

ax2.xaxis.set_major_locator(ticker.AutoLocator())
ax2.xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax2.yaxis.set_major_locator(ticker.AutoLocator())
ax2.yaxis.set_minor_locator(ticker.AutoMinorLocator())

ax2.scatter(mag_inst_f115w, delta_y_f115w, s=1, color='gray')
ax2.plot([xlim0, xlim1], [0, 0], color='k', lw=2, ls='--')

ax2.set_xlabel(filt1 + '_inst', fontdict=font2)
ax2.set_ylabel('$\Delta$ Y (px)', fontdict=font2)

ax3 = plt.subplot(2, 2, 3)

ax3.set_xlim(xlim0, xlim1)
ax3.set_ylim(ylim0, ylim1)

ax3.xaxis.set_major_locator(ticker.AutoLocator())
ax3.xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax3.yaxis.set_major_locator(ticker.AutoLocator())
ax3.yaxis.set_minor_locator(ticker.AutoMinorLocator())

mag_inst_f200w = -2.5 * np.log10(results_clean_f200w[0]['flux_fit'])

ax3.scatter(mag_inst_f200w, delta_x_f200w, s=1, color='gray')
ax3.plot([xlim0, xlim1], [0, 0], color='k', lw=2, ls='--')

ax3.set_xlabel(filt2 + '_inst', fontdict=font2)
ax3.set_ylabel('$\Delta$ X (px)', fontdict=font2)

ax4 = plt.subplot(2, 2, 4)

ax4.set_xlim(xlim0, xlim1)
ax4.set_ylim(ylim0, ylim1)

ax4.xaxis.set_major_locator(ticker.AutoLocator())
ax4.xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax4.yaxis.set_major_locator(ticker.AutoLocator())
ax4.yaxis.set_minor_locator(ticker.AutoMinorLocator())

ax4.scatter(mag_inst_f200w, delta_y_f200w, s=1, color='gray')
ax4.plot([xlim0, xlim1], [0, 0], color='k', lw=2, ls='--')

ax4.set_xlabel(filt2 + '_inst', fontdict=font2)
ax4.set_ylabel('$\Delta$ Y (px)', fontdict=font2)

plt.tight_layout()

Cross-match the 4 catalogs for each filter

To obtain a final color-magnitude diagram, we need to cross-match all the catalogs for each filters and then cross-match the derived final catalogs.

Note: this is the most conservative approach since we impose that a star must be found in all 4 catalogs.

Note for developer:

I couldn't find an easier way to write this function, where you need to match the first two catalogs, derive a sub-catalogs with only the matches and then iterate for all the other catalogs available. We should also think on how to create a function that allows to keep the stars also if they are available in X out of Y catalogs (i.e., if for some reasons, a measure is not available in 1 of the images, but the star is well measured in the other 3, it doesn't make sense to discard the object).

In [35]:
def crossmatch_filter(table=None):

    num = 0
    num_cat = np.char.mod('%d', np.arange(1, len(table) + 1, 1))

    idx_12, d2d_12, _ = match_coordinates_sky(table[num]['radec'], table[num + 1]['radec'])

    sep_constraint_12 = d2d_12 < max_sep

    matches_12 = Table()

    matches_12['radec_' + num_cat[num]] = table[num]['radec'][sep_constraint_12]
    matches_12['mag_' + num_cat[num]] = (-2.5 * np.log10(table[num]['flux_fit']))[sep_constraint_12]
    matches_12['emag_' + num_cat[num]] = (1.086 * (table[num]['flux_unc'] / 
                                                   table[num]['flux_fit']))[sep_constraint_12]

    matches_12['radec_' + num_cat[num + 1]] = table[num + 1]['radec'][idx_12[sep_constraint_12]]
    matches_12['mag_' + num_cat[num + 1]] = (-2.5 * np.log10(table[num + 1]['flux_fit']))[idx_12[sep_constraint_12]]
    matches_12['emag_' + num_cat[num + 1]] = (1.086 * (table[num + 1]['flux_unc'] /
                                                       table[num + 1]['flux_fit']))[idx_12[sep_constraint_12]]

    idx_123, d2d_123, _ = match_coordinates_sky(matches_12['radec_' + num_cat[num]], table[num + 2]['radec'])

    sep_constraint_123 = d2d_123 < max_sep

    matches_123 = Table()

    matches_123['radec_' + num_cat[num]] = matches_12['radec_' + num_cat[num]][sep_constraint_123]
    matches_123['mag_' + num_cat[num]] = matches_12['mag_' + num_cat[num]][sep_constraint_123]
    matches_123['emag_' + num_cat[num]] = matches_12['emag_' + num_cat[num]][sep_constraint_123]
    matches_123['radec_' + num_cat[num + 1]] = matches_12['radec_' + num_cat[num + 1]][sep_constraint_123]
    matches_123['mag_' + num_cat[num + 1]] = matches_12['mag_' + num_cat[num + 1]][sep_constraint_123]
    matches_123['emag_' + num_cat[num + 1]] = matches_12['emag_' + num_cat[num + 1]][sep_constraint_123]
    matches_123['radec_' + num_cat[num + 2]] = table[num + 2]['radec'][idx_123[sep_constraint_123]]
    matches_123['mag_' + num_cat[num + 2]] = (-2.5 * np.log10(table[num + 2]['flux_fit']))[idx_123[sep_constraint_123]]
    matches_123['emag_' + num_cat[num + 2]] = (1.086 * (table[num + 2]['flux_unc'] /
                                                        table[num + 2]['flux_fit']))[idx_123[sep_constraint_123]]

    idx_1234, d2d_1234, _ = match_coordinates_sky(matches_123['radec_' + num_cat[num]], table[num + 3]['radec'])

    sep_constraint_1234 = d2d_1234 < max_sep

    matches_1234 = Table()

    matches_1234['radec_' + num_cat[num]] = matches_123['radec_' + num_cat[num]][sep_constraint_1234]
    matches_1234['mag_' + num_cat[num]] = matches_123['mag_' + num_cat[num]][sep_constraint_1234]
    matches_1234['emag_' + num_cat[num]] = matches_123['emag_' + num_cat[num]][sep_constraint_1234]
    matches_1234['radec_' + num_cat[num + 1]] = matches_123['radec_' + num_cat[num + 1]][sep_constraint_1234]
    matches_1234['mag_' + num_cat[num + 1]] = matches_123['mag_' + num_cat[num + 1]][sep_constraint_1234]
    matches_1234['emag_' + num_cat[num + 1]] = matches_123['emag_' + num_cat[num + 1]][sep_constraint_1234]
    matches_1234['radec_' + num_cat[num + 2]] = matches_123['radec_' + num_cat[num + 2]][sep_constraint_1234]
    matches_1234['mag_' + num_cat[num + 2]] = matches_123['mag_' + num_cat[num + 2]][sep_constraint_1234]
    matches_1234['emag_' + num_cat[num + 2]] = matches_123['emag_' + num_cat[num + 2]][sep_constraint_1234]
    matches_1234['radec_' + num_cat[num + 3]] = table[num + 3]['radec'][idx_1234[sep_constraint_1234]]
    matches_1234['mag_' + num_cat[num + 3]] = (-2.5 * np.log10(table[num + 3]['flux_fit']))[idx_1234[sep_constraint_1234]]
    matches_1234['emag_' + num_cat[num + 3]] = (1.086 * (table[num + 3]['flux_unc'] /
                                                         table[num + 3]['flux_fit']))[idx_1234[sep_constraint_1234]]

    matches_1234

    return matches_1234
In [36]:
matches_f115w = crossmatch_filter(table=results_clean_f115w)
matches_f200w = crossmatch_filter(table=results_clean_f200w)

For the final catalog, we assume that the magnitude is the mean of the 4 measures and the error on the magnitude is its standard deviation.

To easily perform this arithmetic operation on the table, we convert the table to pandas dataframe.

In [37]:
df_f115w = matches_f115w.to_pandas()
df_f200w = matches_f200w.to_pandas()

df_f115w['RA_' + filt1] = df_f115w[['radec_1.ra', 'radec_2.ra', 'radec_3.ra', 'radec_4.ra']].mean(axis=1)
df_f115w['e_RA_' + filt1] = df_f115w[['radec_1.ra', 'radec_2.ra', 'radec_3.ra', 'radec_4.ra']].std(axis=1)
df_f115w['Dec_' + filt1] = df_f115w[['radec_1.dec', 'radec_2.dec', 'radec_3.dec', 'radec_4.dec']].mean(axis=1)
df_f115w['e_Dec_' + filt1] = df_f115w[['radec_1.dec', 'radec_2.dec', 'radec_3.dec', 'radec_4.dec']].std(axis=1)
df_f115w[filt1 + '_inst'] = df_f115w[['mag_1', 'mag_2', 'mag_3', 'mag_4']].mean(axis=1)
df_f115w['e' + filt1 + '_inst'] = df_f115w[['mag_1', 'mag_2', 'mag_3', 'mag_4']].std(axis=1)

df_f200w['RA_' + filt2] = df_f200w[['radec_1.ra', 'radec_2.ra', 'radec_3.ra', 'radec_4.ra']].mean(axis=1)
df_f200w['e_RA_' + filt2] = df_f200w[['radec_1.ra', 'radec_2.ra', 'radec_3.ra', 'radec_4.ra']].std(axis=1)
df_f200w['Dec_' + filt2] = df_f200w[['radec_1.dec', 'radec_2.dec', 'radec_3.dec', 'radec_4.dec']].mean(axis=1)
df_f200w['e_Dec_' + filt2] = df_f200w[['radec_1.dec', 'radec_2.dec', 'radec_3.dec', 'radec_4.dec']].std(axis=1)
df_f200w[filt2 + '_inst'] = df_f200w[['mag_1', 'mag_2', 'mag_3', 'mag_4']].mean(axis=1)
df_f200w['e' + filt2 + '_inst'] = df_f200w[['mag_1', 'mag_2', 'mag_3', 'mag_4']].std(axis=1)

Final Color-Magnitude Diagram (Instrumental Magnitudes)

In [38]:
plt.figure(figsize=(12, 14))
plt.clf()

ax1 = plt.subplot(1, 2, 1)

ax1.set_xlabel(filt1 + '_inst -' + filt2 + '_inst', fontdict=font2)
ax1.set_ylabel(filt1 + '_inst', fontdict=font2)

xlim0 = -0.5
xlim1 = 0.8
ylim0 = -1.5
ylim1 = -9

ax1.set_xlim(xlim0, xlim1)
ax1.set_ylim(ylim0, ylim1)

ax1.xaxis.set_major_locator(ticker.AutoLocator())
ax1.xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax1.yaxis.set_major_locator(ticker.AutoLocator())
ax1.yaxis.set_minor_locator(ticker.AutoMinorLocator())

radec_f115w_inst = SkyCoord(df_f115w['RA_' + filt1], df_f115w['Dec_' + filt1], unit='deg')
radec_f200w_inst = SkyCoord(df_f200w['RA_' + filt2], df_f200w['Dec_' + filt2], unit='deg')

idx_inst, d2d_inst, _ = match_coordinates_sky(radec_f115w_inst, radec_f200w_inst)

sep_constraint_inst = d2d_inst < max_sep

f115w_inst = np.array(df_f115w[filt1 + '_inst'][sep_constraint_inst])
ef115w_inst = np.array(df_f115w['e' + filt1 + '_inst'][sep_constraint_inst])
radec_f115w = radec_f115w_inst[sep_constraint_inst]

f200w_inst = np.array(df_f200w[filt2 + '_inst'][idx_inst[sep_constraint_inst]])
ef200w_inst = np.array(df_f200w['e' + filt2 + '_inst'][idx_inst[sep_constraint_inst]])
radec_f200w = radec_f200w_inst[idx_inst[sep_constraint_inst]]

ax1.scatter(f115w_inst - f200w_inst, f115w_inst, s=1, color='k')

ax2 = plt.subplot(2, 2, 2)

ax2.set_xlabel(filt1 + '_inst', fontdict=font2)
ax2.set_ylabel('$\sigma$' + filt1, fontdict=font2)

xlim0 = -9
xlim1 = -1.5
ylim0 = -0.01 
ylim1 = 1

ax2.set_xlim(xlim0, xlim1)
ax2.set_ylim(ylim0, ylim1)

ax2.xaxis.set_major_locator(ticker.AutoLocator())
ax2.xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax2.yaxis.set_major_locator(ticker.AutoLocator())
ax2.yaxis.set_minor_locator(ticker.AutoMinorLocator())

ax2.scatter(df_f115w[filt1 + '_inst'], df_f115w['e' + filt1 + '_inst'], s=1, color='k')

ax3 = plt.subplot(2, 2, 4)

ax3.set_xlabel(filt2 + '_inst', fontdict=font2)
ax3.set_ylabel('$\sigma$' + filt2, fontdict=font2)

ax3.set_xlim(xlim0, xlim1)
ax3.set_ylim(ylim0, ylim1)

ax3.xaxis.set_major_locator(ticker.AutoLocator())
ax3.xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax3.yaxis.set_major_locator(ticker.AutoLocator())
ax3.yaxis.set_minor_locator(ticker.AutoMinorLocator())

ax3.scatter(df_f200w[filt2 + '_inst'], df_f200w['e' + filt2 + '_inst'], s=1, color='k')

plt.tight_layout()

Photometric Zeropoints

To obtain the final calibrated color-magnitude diagram, we need to calculate the photometric zeropoints. Hence we need to perform aperture photometry on the calibrated images (Level-3), apply the appropriate aperture correction for the finite aperture adopted (the values provided in the dictionary above are for an infinite aperture) and then compare it with the PSF photometry. Hence, we can summarize the steps as follows:

  • perform aperture photometry
  • apply appropriate aperture correction
  • apply tabulated zeropoint
  • cross-match with psf photometry

Load the calibrated and rectified images (Level 3 imaging pipeline)

In [39]:
dict_images_combined = {'NRCA1': {}, 'NRCA2': {}, 'NRCA3': {}, 'NRCA4': {}, 'NRCA5': {},
                        'NRCB1': {}, 'NRCB2': {}, 'NRCB3': {}, 'NRCB4': {}, 'NRCB5': {}}

dict_filter_short = {}
dict_filter_long = {}

ff_short = []
det_short = []
det_long = []
ff_long = []
detlist_short = []
detlist_long = []
filtlist_short = []
filtlist_long = []

if not glob.glob('./*combined*fits'):

    print("Downloading images")

    boxlink_images_lev3 = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/stellar_photometry/images_level3.tar.gz'
    boxfile_images_lev3 = './images_level3.tar.gz'
    urllib.request.urlretrieve(boxlink_images_lev3, boxfile_images_lev3)

    tar = tarfile.open(boxfile_images_lev3, 'r')
    tar.extractall()

    images_dir = './'
    files_singles = sorted(glob.glob(os.path.join(images_dir, "*combined*fits")))

else:

    images_dir = './'
    files_singles = sorted(glob.glob(os.path.join(images_dir, "*combined*fits")))

for file in files_singles:

    im = fits.open(file)
    f = im[0].header['FILTER']
    d = im[0].header['DETECTOR']

    if d == 'NRCBLONG':
        d = 'NRCB5'
    elif d == 'NRCALONG':
        d = 'NRCA5'
    else:
        d = d

    wv = np.float(f[1:3])

    if wv > 24:
        ff_long.append(f)
        det_long.append(d)

    else:
        ff_short.append(f)
        det_short.append(d)

    detlist_short = sorted(list(dict.fromkeys(det_short)))
    detlist_long = sorted(list(dict.fromkeys(det_long)))

    unique_list_filters_short = []
    unique_list_filters_long = []

    for x in ff_short:

        if x not in unique_list_filters_short:

            dict_filter_short.setdefault(x, {})

    for x in ff_long:
        if x not in unique_list_filters_long:
            dict_filter_long.setdefault(x, {})

    for d_s in detlist_short:
        dict_images_combined[d_s] = dict_filter_short

    for d_l in detlist_long:
        dict_images_combined[d_l] = dict_filter_long

    filtlist_short = sorted(list(dict.fromkeys(dict_filter_short)))
    filtlist_long = sorted(list(dict.fromkeys(dict_filter_long)))

    if len(dict_images_combined[d][f]) == 0:
        dict_images_combined[d][f] = {'images': [file]}
    else:
        dict_images_combined[d][f]['images'].append(file)

print("Available Detectors for SW channel:", detlist_short)
print("Available Detectors for LW channel:", detlist_long)
print("Available SW Filters:", filtlist_short)
print("Available LW Filters:", filtlist_long)
Downloading images
Available Detectors for SW channel: ['NRCB1']
Available Detectors for LW channel: ['NRCB5']
Available SW Filters: ['F070W', 'F115W', 'F200W']
Available LW Filters: ['F277W', 'F356W', 'F444W']

Display the images

In [40]:
plt.figure(figsize=(14, 14))

for det in dets_short:
    for i, filt in enumerate(filts_short):

        image = fits.open(dict_images_combined[det][filt]['images'][0])
        data_sb = image[1].data

        ax = plt.subplot(1, len(filts_short), i + 1)

        norm = simple_norm(data_sb, 'sqrt', percent=99.)
        plt.xlabel("X [px]", fontdict=font2)
        plt.ylabel("Y [px]", fontdict=font2)
        plt.title(filt, fontdict=font2)

        ax.imshow(data_sb, norm=norm, cmap='Greys')
plt.tight_layout()

Aperture Photometry

As we have done previously, we create a dictionary that contains the tables with the derived aperture photometry for each image.

In [41]:
dict_aper = {}

for det in dets_short:

    dict_aper.setdefault(det, {})
    for j, filt in enumerate(filts_short):

        dict_aper[det].setdefault(filt, {})

        dict_aper[det][filt]['stars for ap phot'] = None
        dict_aper[det][filt]['stars for ap phot matched'] = None
        dict_aper[det][filt]['aperture phot table'] = None

Find bright isolated stars

In [42]:
def find_bright_stars(det='NRCA1', filt='F070W', dist_sel=False):

    bkgrms = MADStdBackgroundRMS()
    mmm_bkg = MMMBackground()

    image = fits.open(dict_images_combined[det][filt]['images'][i])
    data_sb = image[1].data
    imh = image[1].header

    print("Selecting stars for aperture photometry on image {number} of filter {f}, detector {d}".format(number=i + 1, f=filt, d=det))

    data = data_sb / imh['PHOTMJSR']
    print("Conversion factor from {units} to DN/s for filter {f}:".format(units=imh['BUNIT'], f=filt), imh['PHOTMJSR'])

    sigma_psf = dict_utils[filt]['psf fwhm']

    print("FWHM for the filter {f}:".format(f=filt), sigma_psf, "px")

    std = bkgrms(data)
    bkg = mmm_bkg(data)
    daofind = DAOStarFinder(threshold=th[j] * std + bkg, fwhm=sigma_psf, roundhi=1.0, roundlo=-1.0,
                            sharplo=0.30, sharphi=1.40)

    apcorr_stars = daofind(data)
    dict_aper[det][filt]['stars for ap phot'] = apcorr_stars
    
    if dist_sel:

        print("")
        print("Calculating closest neigbhour distance")

        d = []

        daofind_tot = DAOStarFinder(threshold=10 * std + bkg, fwhm=sigma_psf, roundhi=1.0, roundlo=-1.0,
                                    sharplo=0.30, sharphi=1.40)

        stars_tot = daofind_tot(data)

        x_tot = stars_tot['xcentroid']
        y_tot = stars_tot['ycentroid']

        for xx, yy in zip(apcorr_stars['xcentroid'], apcorr_stars['ycentroid']):

            sep = []
            dist = np.sqrt((x_tot - xx)**2 + (y_tot - yy)**2)
            sep = np.sort(dist)[1:2][0]
            d.append(sep)

        apcorr_stars['min distance'] = d
        mask_dist = (apcorr_stars['min distance'] > min_sep[j])

        apcorr_stars = apcorr_stars[mask_dist]

        dict_aper[det][filt]['stars for ap phot'] = apcorr_stars

        print("Minimum distance required:", min_sep[j], "px")
        print("")
        print("Number of bright isolated sources found in the image for {f}:".format(f=filt), len(apcorr_stars))
        print("-----------------------------------------------------")
        print("")
    else:
        print("")
        print("Number of bright sources found in the image for {f}:".format(f=filt), len(apcorr_stars))
        print("--------------------------------------------")
        print("")    
    
    return
In [43]:
tic = time.perf_counter()

th = [700, 500]  # threshold level for the two filters (length must match number of filters analyzed)
min_sep = [10, 10]  # minimum separation acceptable for zp stars from closest neighbour


for det in dets_short:
    for j, filt in enumerate(filts_short):
        for i in np.arange(0, len(dict_images_combined[det][filt]['images']), 1):

            find_bright_stars(det=det, filt=filt, dist_sel=False)

toc = time.perf_counter()

print("Elapsed Time for finding stars for Aperture Photometry:", toc - tic)            
Selecting stars for aperture photometry on image 1 of filter F115W, detector NRCB1
Conversion factor from MJy/sr to DN/s for filter F115W: 3.821892261505127
FWHM for the filter F115W: 1.298 px

Number of bright sources found in the image for F115W: 1302
--------------------------------------------

Selecting stars for aperture photometry on image 1 of filter F200W, detector NRCB1
Conversion factor from MJy/sr to DN/s for filter F200W: 2.564082860946655
FWHM for the filter F200W: 2.141 px

Number of bright sources found in the image for F200W: 1483
--------------------------------------------

Elapsed Time for finding stars for Aperture Photometry: 8.009523651002382

As a further way to obtain a good quality sample, we cross-match the catalogs from the two filters and retain only the stars in common

In [44]:
for det in dets_short:
    for j, filt in enumerate(filts_short):
        for i in np.arange(0, len(dict_images_combined[det][filt]['images']), 1):

            image = ImageModel(dict_images_combined[det][filt]['images'][i])

            ra, dec = image.meta.wcs(dict_aper[det][filt]['stars for ap phot']['xcentroid'],
                                     dict_aper[det][filt]['stars for ap phot']['ycentroid'])
        
            radec = SkyCoord(ra, dec, unit='deg')
            dict_aper[det][filt]['stars for ap phot']['radec'] = radec
In [45]:
idx_ap, d2d_ap, _ = match_coordinates_sky(dict_aper[det][filt1]['stars for ap phot']['radec'],
                                          dict_aper[det][filt2]['stars for ap phot']['radec'])

sep_constraint_ap = d2d_ap < max_sep

matched_apcorr_f115w = Table()
matched_apcorr_f200w = Table()

matched_apcorr_f115w = dict_aper[det][filt1]['stars for ap phot'][sep_constraint_ap]
matched_apcorr_f200w = dict_aper[det][filt2]['stars for ap phot'][idx_ap[sep_constraint_ap]]

dict_aper[det][filt1]['stars for ap phot matched'] = matched_apcorr_f115w
dict_aper[det][filt2]['stars for ap phot matched'] = matched_apcorr_f200w

Load aperture correction table

Note: these values are obtained from the study of the synthetic WebbPSF PSFs. They will be updated once we have in-flight measures.

In [46]:
if os.path.isfile('./aperture_correction_table.txt'):
    ap_tab = './aperture_correction_table.txt'
else:
    print("Downloading the aperture correction table")

    boxlink_apcorr_table = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/stellar_photometry/aperture_correction_table.txt'
    boxfile_apcorr_table = './aperture_correction_table.txt'
    urllib.request.urlretrieve(boxlink_apcorr_table, boxfile_apcorr_table)
    ap_tab = './aperture_correction_table.txt'

aper_table = pd.read_csv(ap_tab, header=None, sep='\s+', index_col=0,
                         names=['filter', 'pupil', 'wave', 'r10', 'r20', 'r30', 'r40', 'r50', 'r60', 'r70', 'r80',
                                'r85', 'r90', 'sky_flux_px', 'apcorr10', 'apcorr20', 'apcorr30', 'apcorr40',
                                'apcorr50', 'apcorr60', 'apcorr70', 'apcorr80', 'apcorr85', 'apcorr90', 'sky_in',
                                'sky_out'], comment='#', skiprows=0, usecols=range(0, 26))
aper_table.head()
Downloading the aperture correction table
Out[46]:
pupil wave r10 r20 r30 r40 r50 r60 r70 r80 ... apcorr30 apcorr40 apcorr50 apcorr60 apcorr70 apcorr80 apcorr85 apcorr90 sky_in sky_out
filter
F070W CLEAR 70 0.451 0.869 1.263 1.648 2.191 3.266 5.176 7.292 ... 3.3651 2.5305 2.0347 1.7210 1.5328 1.4174 1.4174 1.5880 7.292 9.017
F090W CLEAR 90 0.408 0.638 0.992 1.503 1.925 2.549 4.162 7.480 ... 3.3519 2.5241 2.0253 1.6977 1.4908 1.4173 1.4173 1.6016 7.480 9.251
F115W CLEAR 115 0.374 0.571 0.778 1.036 1.768 2.324 3.287 6.829 ... 3.3404 2.5070 2.0131 1.6825 1.4520 1.3310 1.3310 1.3964 6.829 9.723
F140M CLEAR 140 0.414 0.617 0.801 1.031 1.367 2.434 3.118 6.031 ... 3.3397 2.5060 2.0067 1.6815 1.4465 1.3029 1.3029 1.3794 6.031 9.608
F150W CLEAR 150 0.431 0.639 0.826 1.065 1.360 2.476 3.199 6.082 ... 3.3405 2.5067 2.0070 1.6828 1.4485 1.3068 1.3068 1.4188 6.082 9.496

5 rows × 25 columns

Perform Aperture Photometry

In [47]:
def aperture_phot(det=det, filt='F070W'):

    radii = [aper_table.loc[filt]['r70']]

    ees = '70'.split()
    ee_radii = dict(zip(ees, radii))

    positions = np.transpose((dict_aper[det][filt]['stars for ap phot matched']['xcentroid'],
                              dict_aper[det][filt]['stars for ap phot matched']['ycentroid']))

    image = fits.open(dict_images_combined[det][filt]['images'][0])
    data_sb = image[1].data
    imh = image[1].header
    data = data_sb / imh['PHOTMJSR']

    # sky from the aperture correction table:

    sky = {"sky_in": aper_table.loc[filt]['r80'], "sky_out": aper_table.loc[filt]['r85']}

    tic = time.perf_counter()

    table_aper = Table()

    for ee, radius in ee_radii.items():
        print("Performing aperture photometry for radius equivalent to EE = {0}% for filter {1}".format(ee, filt))
        aperture = CircularAperture(positions, r=radius)
        annulus_aperture = CircularAnnulus(positions, r_in=sky["sky_in"], r_out=sky["sky_out"])
        annulus_mask = annulus_aperture.to_mask(method='center')

        bkg_median = []
        for mask in annulus_mask:
            annulus_data = mask.multiply(data)
            annulus_data_1d = annulus_data[mask.data > 0]
            _, median_sigclip, _ = sigma_clipped_stats(annulus_data_1d)
            bkg_median.append(median_sigclip)
        bkg_median = np.array(bkg_median)

        phot = aperture_photometry(data, aperture, method='exact')
        phot['annulus_median'] = bkg_median
        phot['aper_bkg'] = bkg_median * aperture.area
        phot['aper_sum_bkgsub'] = phot['aperture_sum'] - phot['aper_bkg']

        apcorr = [aper_table.loc[filt]['apcorr70']]

        phot['aper_sum_corrected'] = phot['aper_sum_bkgsub'] * apcorr

        phot['mag_corrected'] = -2.5 * np.log10(phot['aper_sum_corrected']) + dict_utils[filt]['VegaMAG zp modB']

        table_aper.add_column(phot['aperture_sum'], name='aper_sum_' + ee)
        table_aper.add_column(phot['annulus_median'], name='annulus_median_' + ee)
        table_aper.add_column(phot['aper_bkg'], name='aper_bkg_ee_' + ee)
        table_aper.add_column(phot['aper_sum_bkgsub'], name='aper_sum_bkgsub_' + ee)
        table_aper.add_column(phot['aper_sum_corrected'], name='aper_sum_corrected_' + filt) 
        table_aper.add_column(phot['mag_corrected'], name='mag_corrected_' + filt)

        dict_aper[det][filt]['aperture phot table'] = table_aper

    toc = time.perf_counter()
    print("Time Elapsed:", toc - tic)

    return
In [48]:
aperture_phot(det=det, filt=filt1)
aperture_phot(det=det, filt=filt2)
Performing aperture photometry for radius equivalent to EE = 70% for filter F115W
Time Elapsed: 0.7199397270014742
Performing aperture photometry for radius equivalent to EE = 70% for filter F200W
Time Elapsed: 0.4724022779992083

Derive Zeropoints

In [49]:
plt.figure(figsize=(14, 8))
plt.clf()

ax1 = plt.subplot(2, 1, 1)

ax1.set_xlabel(filt1, fontdict=font2)
ax1.set_ylabel('Zeropoint', fontdict=font2)

idx_zp_1, d2d_zp_1, _ = match_coordinates_sky(dict_aper[det][filt1]['stars for ap phot matched']['radec'], radec_f115w_inst)

sep_constraint_zp_1 = d2d_zp_1 < max_sep

f115w_ap_matched = np.array(dict_aper[det][filt1]['aperture phot table']['mag_corrected_' + filt1][sep_constraint_zp_1])
f115w_psf_matched = np.array(df_f115w[filt1 + '_inst'][idx_zp_1[sep_constraint_zp_1]])

diff_f115w = f115w_ap_matched - f115w_psf_matched
_, zp_f115w, zp_sigma_f115w = sigma_clipped_stats(diff_f115w)

xlim0 = -9
xlim1 = -5
ylim0 = np.mean(diff_f115w) - 0.5
ylim1 = np.mean(diff_f115w) + 0.5

ax1.set_xlim(xlim0, xlim1)
ax1.set_ylim(ylim0, ylim1)

ax1.xaxis.set_major_locator(ticker.AutoLocator())
ax1.xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax1.yaxis.set_major_locator(ticker.AutoLocator())
ax1.yaxis.set_minor_locator(ticker.AutoMinorLocator())

ax1.scatter(f115w_psf_matched, diff_f115w, s=50, color='k')
ax1.plot([xlim0, xlim1], [zp_f115w, zp_f115w], color='r', lw=5, ls='--')
ax1.text(xlim0 + 0.05, ylim1 - 0.15, filt1 + ' Zeropoint = %5.3f $\pm$ %5.3f' % (zp_f115w, zp_sigma_f115w), color='k', fontdict=font2)
                
ax2 = plt.subplot(2, 1, 2)

ax2.set_xlabel(filt2, fontdict=font2)
ax2.set_ylabel('Zeropoint', fontdict=font2)

idx_zp_2, d2d_zp_2, _ = match_coordinates_sky(dict_aper[det][filt2]['stars for ap phot matched']['radec'], radec_f200w_inst)

sep_constraint_zp_2 = d2d_zp_2 < max_sep

f200w_ap_matched = np.array(dict_aper[det][filt2]['aperture phot table']['mag_corrected_' + filt2][sep_constraint_zp_2])
f200w_psf_matched = np.array(df_f200w[filt2 + '_inst'][idx_zp_2[sep_constraint_zp_2]])

diff_f200w = f200w_ap_matched - f200w_psf_matched
_, zp_f200w, zp_sigma_f200w = sigma_clipped_stats(diff_f200w)

xlim0 = -9
xlim1 = -5
ylim0 = np.mean(diff_f200w) - 0.5
ylim1 = np.mean(diff_f200w) + 0.5

ax2.set_xlim(xlim0, xlim1)
ax2.set_ylim(ylim0, ylim1)

ax2.xaxis.set_major_locator(ticker.AutoLocator())
ax2.xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax2.yaxis.set_major_locator(ticker.AutoLocator())
ax2.yaxis.set_minor_locator(ticker.AutoMinorLocator())

ax2.scatter(f200w_psf_matched, diff_f200w, s=50, color='k')
ax2.plot([xlim0, xlim1], [zp_f200w, zp_f200w], color='r', lw=5, ls='--')
ax2.text(xlim0 + 0.05, ylim1 - 0.15, filt2 + ' Zeropoint = %5.3f $\pm$ %5.3f' % (zp_f200w, zp_sigma_f200w), color='k', fontdict=font2)
                
plt.tight_layout()

Import input photometry

In [50]:
if os.path.isfile('./pointsource.cat'):
    input_cat = './pointsource.cat'

else:
    
    print("Downloading input pointsource catalog")

    boxlink_input_cat = 'https://data.science.stsci.edu/redirect/JWST/jwst-data_analysis_tools/stellar_photometry/pointsource.cat'
    boxfile_input_cat = './pointsource.cat'
    urllib.request.urlretrieve(boxlink_input_cat, boxfile_input_cat)
    input_cat = './pointsource.cat'

cat = pd.read_csv(input_cat, header=None, sep='\s+', names=['ra_in', 'dec_in', 'f070w_in', 'f115w_in',
                                                            'f200w_in', 'f277w_in', 'f356w_in', 'f444w_in'],
                  comment='#', skiprows=7, usecols=range(0, 8))

cat.head()
Downloading input pointsource catalog
Out[50]:
ra_in dec_in f070w_in f115w_in f200w_in f277w_in f356w_in f444w_in
0 80.386396 -69.468909 21.34469 20.75333 20.25038 20.23116 20.20482 20.23520
1 80.385588 -69.469201 20.12613 19.33709 18.64676 18.63521 18.58796 18.64291
2 80.380365 -69.470930 21.52160 20.98518 20.53500 20.51410 20.49231 20.51610
3 80.388130 -69.468453 20.82162 20.06542 19.40552 19.39262 19.34927 19.40018
4 80.388936 -69.468196 21.47197 20.92519 20.46507 20.44447 20.42186 20.44687

Extract from the input catalog the stars in the same region as the one analyzed

In [51]:
lim_ra_min = np.min(radec_f115w.ra)
lim_ra_max = np.max(radec_f115w.ra)
lim_dec_min = np.min(radec_f115w.dec)
lim_dec_max = np.max(radec_f115w.dec)

cat_sel = cat[(cat['ra_in'] > lim_ra_min) & (cat['ra_in'] < lim_ra_max) & (cat['dec_in'] > lim_dec_min)
              & (cat['dec_in'] < lim_dec_max)]

Calibrated Color-Magnitude Diagram

In [52]:
plt.figure(figsize=(12, 14))
plt.clf()

ax1 = plt.subplot(1, 2, 1)

mag1_in = np.array(cat_sel['f115w_in'])
mag2_in = np.array(cat_sel['f200w_in'])
diff_in = mag1_in - mag2_in

xlim0 = -0.25
xlim1 = 1.75
ylim0 = 25
ylim1 = 15 
ax1.set_xlim(xlim0, xlim1)
ax1.set_ylim(ylim0, ylim1)

ax1.xaxis.set_major_locator(ticker.AutoLocator())
ax1.xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax1.yaxis.set_major_locator(ticker.AutoLocator())
ax1.yaxis.set_minor_locator(ticker.AutoMinorLocator())

ax1.scatter(mag1_in - mag2_in, mag1_in, s=1, color='k')

ax1.set_xlabel(filt1 + ' - ' + filt2, fontdict=font2)
ax1.set_ylabel(filt1, fontdict=font2)
ax1.text(xlim0 + 0.15, 15.5, "Input", fontdict=font2)

ax2 = plt.subplot(1, 2, 2)

ax2.set_xlim(xlim0, xlim1)
ax2.set_ylim(ylim0, ylim1)

ax2.xaxis.set_major_locator(ticker.AutoLocator())
ax2.xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax2.yaxis.set_major_locator(ticker.AutoLocator())
ax2.yaxis.set_minor_locator(ticker.AutoMinorLocator())

f115w = f115w_inst + zp_f115w 
f200w = f200w_inst + zp_f200w

maglim = np.arange(18, 25, 1)
mags = []
errs_mag = []
errs_col = []

for i in np.arange(0, len(maglim) - 1, 1):

    mag = (maglim[i] + maglim[i + 1]) / 2
    err_mag1 = ef115w_inst[(f115w > maglim[i]) & (f115w < maglim[i + 1])]
    err_mag2 = ef200w_inst[(f115w > maglim[i]) & (f115w < maglim[i + 1])]
    err_mag = np.mean(err_mag1[i])
    err_temp = np.sqrt(err_mag1**2 + err_mag2**2)
    err_col = np.mean(err_temp[i])

    errs_mag.append(err_mag)                  
    errs_col.append(err_col)
    mags.append(mag)

col = [0] * (len(maglim) - 1)

ax2.errorbar(col, mags, yerr=errs_mag, xerr=errs_col, fmt='o', color='k')
        
ax2.scatter(f115w - f200w, f115w, s=1, color='k')
ax2.text(xlim0 + 0.15, 15.5, "Output", fontdict=font2)

ax2.set_xlabel(filt1 + ' - ' + filt2, fontdict=font2)
ax2.set_ylabel(filt1, fontdict=font2)

plt.tight_layout()

Comparison between input and output photometry

In [53]:
plt.figure(figsize=(14, 8))
plt.clf()

ax1 = plt.subplot(2, 1, 1)

ax1.set_xlabel(filt1, fontdict=font2)
ax1.set_ylabel('$\Delta$ Mag', fontdict=font2)

radec_input = SkyCoord(cat_sel['ra_in'], cat_sel['dec_in'], unit='deg')

idx_f115w_cfr, d2d_f115w_cfr, _ = match_coordinates_sky(radec_input, radec_f115w)

sep_f115w_cfr = d2d_f115w_cfr < max_sep

f115w_inp_cfr = np.array(cat_sel['f115w_in'][sep_f115w_cfr])
f115w_psf_cfr = np.array(f115w[idx_f115w_cfr[sep_f115w_cfr]])

diff_f115w_cfr = f115w_inp_cfr - f115w_psf_cfr
_, med_diff_f115w_cfr, sig_diff_f115w_cfr = sigma_clipped_stats(diff_f115w_cfr)

xlim0 = 16
xlim1 = 24.5
ylim0 = np.mean(diff_f115w_cfr) - 0.5
ylim1 = np.mean(diff_f115w_cfr) + 0.5

ax1.set_xlim(xlim0, xlim1)
ax1.set_ylim(ylim0, ylim1)

ax1.xaxis.set_major_locator(ticker.AutoLocator())
ax1.xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax1.yaxis.set_major_locator(ticker.AutoLocator())
ax1.yaxis.set_minor_locator(ticker.AutoMinorLocator())

ax1.scatter(f115w_psf_cfr, diff_f115w_cfr, s=5, color='k')
ax1.plot([xlim0, xlim1], [0, 0], color='r', lw=5, ls='--')
ax1.text(xlim0 + 0.05, ylim1 - 0.15, filt1 + ' $\Delta$ Mag = %5.3f $\pm$ %5.3f'
         % (med_diff_f115w_cfr, sig_diff_f115w_cfr), color='k', fontdict=font2)

ax2 = plt.subplot(2, 1, 2)

ax2.set_xlabel(filt2, fontdict=font2)
ax2.set_ylabel('$\Delta$ Mag', fontdict=font2)

idx_f200w_cfr, d2d_f200w_cfr, _ = match_coordinates_sky(radec_input, radec_f200w)

sep_f200w_cfr = d2d_f200w_cfr < max_sep

f200w_inp_cfr = np.array(cat_sel['f200w_in'][sep_f200w_cfr])
f200w_psf_cfr = np.array(f200w[idx_f200w_cfr[sep_f200w_cfr]])

diff_f200w_cfr = f200w_inp_cfr - f200w_psf_cfr
_, med_diff_f200w_cfr, sig_diff_f200w_cfr = sigma_clipped_stats(diff_f200w_cfr)

xlim0 = 16
xlim1 = 24
ylim0 = np.mean(diff_f200w_cfr) - 0.5 
ylim1 = np.mean(diff_f200w_cfr) + 0.5

ax2.set_xlim(xlim0, xlim1)
ax2.set_ylim(ylim0, ylim1)

ax2.xaxis.set_major_locator(ticker.AutoLocator())
ax2.xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax2.yaxis.set_major_locator(ticker.AutoLocator())
ax2.yaxis.set_minor_locator(ticker.AutoMinorLocator())

ax2.scatter(f200w_psf_cfr, diff_f200w_cfr, s=5, color='k')
ax2.plot([xlim0, xlim1], [0, 0], color='r', lw=5, ls='--')
ax2.text(xlim0 + 0.05, ylim1 - 0.15, filt2 + ' $\Delta$ Mag = %5.3f $\pm$ %5.3f'
         % (med_diff_f200w_cfr, sig_diff_f200w_cfr), color='k', fontdict=font2)

plt.tight_layout()
In [54]:
plt.figure(figsize=(12, 6))

ax1 = plt.subplot(1, 2, 1)

xlim0 = -10
xlim1 = 10
ylim0 = -10
ylim1 = 10

ax1.set_xlim(xlim0, xlim1)
ax1.set_ylim(ylim0, ylim1)

ax1.xaxis.set_major_locator(ticker.AutoLocator())
ax1.xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax1.yaxis.set_major_locator(ticker.AutoLocator())
ax1.yaxis.set_minor_locator(ticker.AutoMinorLocator())

ax1.set_xlabel('$\Delta$ RA (mas)', fontdict=font2)
ax1.set_ylabel('$\Delta$ Dec (mas)', fontdict=font2)
ax1.set_title(filt1, fontdict=font2)

ra_f115w_inp_cfr = np.array(cat_sel['ra_in'][sep_f115w_cfr])
ra_f115w_psf_cfr = np.array(radec_f115w.ra[idx_f115w_cfr[sep_f115w_cfr]])

dec_f115w_inp_cfr = np.array(cat_sel['dec_in'][sep_f115w_cfr])
dec_f115w_psf_cfr = np.array(radec_f115w.dec[idx_f115w_cfr[sep_f115w_cfr]])

dec_rad_f115w = np.radians(dec_f115w_psf_cfr)

diffra_f115w_cfr = ((((ra_f115w_inp_cfr - ra_f115w_psf_cfr) * np.cos(dec_rad_f115w)) * u.deg).to(u.mas) / (1 * u.mas))

_, med_diffra_f115w_cfr, sig_diffra_f115w_cfr = sigma_clipped_stats(diffra_f115w_cfr)

diffdec_f115w_cfr = (((dec_f115w_inp_cfr - dec_f115w_psf_cfr) * u.deg).to(u.mas) / (1 * u.mas))

_, med_diffdec_f115w_cfr, sig_diffdec_f115w_cfr = sigma_clipped_stats(diffdec_f115w_cfr)

ax1.scatter(diffra_f115w_cfr, diffdec_f115w_cfr, s=1, color='k')
ax1.plot([0, 0], [ylim0, ylim1], color='k', lw=2, ls='--')
ax1.plot([xlim0, xlim1], [0, 0], color='k', lw=2, ls='--')

ax1.text(xlim0 + 0.05, ylim1 - 1.50, ' $\Delta$ RA (mas) = %5.3f $\pm$ %5.3f'
         % (med_diffra_f115w_cfr, sig_diffra_f115w_cfr), color='k', fontdict=font2)
ax1.text(xlim0 + 0.05, ylim1 - 3.0, ' $\Delta$ Dec (mas) = %5.3f $\pm$ %5.3f'
         % (med_diffdec_f115w_cfr, sig_diffdec_f115w_cfr), color='k', fontdict=font2)

ax2 = plt.subplot(1, 2, 2)

xlim0 = -10
xlim1 = 10
ylim0 = -10
ylim1 = 10

ax2.set_xlim(xlim0, xlim1)
ax2.set_ylim(ylim0, ylim1)
ax2.set_title(filt2, fontdict=font2)

ax2.xaxis.set_major_locator(ticker.AutoLocator())
ax2.xaxis.set_minor_locator(ticker.AutoMinorLocator())
ax2.yaxis.set_major_locator(ticker.AutoLocator())
ax2.yaxis.set_minor_locator(ticker.AutoMinorLocator())

ax2.set_xlabel('$\Delta$ RA (mas)', fontdict=font2)
ax2.set_ylabel('$\Delta$ Dec (mas)', fontdict=font2)

ra_f200w_inp_cfr = np.array(cat_sel['ra_in'][sep_f200w_cfr])
ra_f200w_psf_cfr = np.array(radec_f200w.ra[idx_f200w_cfr[sep_f200w_cfr]])

dec_f200w_inp_cfr = np.array(cat_sel['dec_in'][sep_f200w_cfr])
dec_f200w_psf_cfr = np.array(radec_f200w.dec[idx_f200w_cfr[sep_f200w_cfr]])

dec_rad_f200w = np.radians(dec_f200w_psf_cfr)

diffra_f200w_cfr = ((((ra_f200w_inp_cfr - ra_f200w_psf_cfr) * np.cos(dec_rad_f200w)) * u.deg).to(u.mas) / (1 * u.mas))

_, med_diffra_f200w_cfr, sig_diffra_f200w_cfr = sigma_clipped_stats(diffra_f200w_cfr)

diffdec_f200w_cfr = (((dec_f200w_inp_cfr - dec_f200w_psf_cfr) * u.deg).to(u.mas) / (1 * u.mas))

_, med_diffdec_f200w_cfr, sig_diffdec_f200w_cfr = sigma_clipped_stats(diffdec_f200w_cfr)

ax2.scatter(diffra_f200w_cfr, diffdec_f200w_cfr, s=1, color='k')
ax2.plot([0, 0], [ylim0, ylim1], color='k', lw=2, ls='--')
ax2.plot([xlim0, xlim1], [0, 0], color='k', lw=2, ls='--')

ax2.text(xlim0 + 0.05, ylim1 - 1.50, ' $\Delta$ RA (mas) = %5.3f $\pm$ %5.3f'
         % (med_diffra_f200w_cfr, sig_diffra_f200w_cfr), color='k', fontdict=font2)
ax2.text(xlim0 + 0.05, ylim1 - 3.0, ' $\Delta$ Dec (mas) = %5.3f $\pm$ %5.3f'
         % (med_diffdec_f200w_cfr, sig_diffdec_f200w_cfr), color='k', fontdict=font2)

plt.tight_layout()

Final notes

This notebook provides a general overview on how to perform PSF photometry using the PhotUtils package. The choice of the different parameters adopted in all the reduction steps as well as the choice of the PSF model depend on the specific user science case. Moreover, a detailed analysis that allow to provide recommendations on how to set those parameters and outline the differences in the output photometry when different PSF models are adopted (single vs PSF grid, number of PSFs in the grid, etc.) will be possible only when real data will be available after the instrument commissioning. In this context, we note that one of the selected ERS program (ERS 1334 - The Resolved Stellar Populations Early Release Science Program) will provide a fundamental test benchmark to explore how the different choices outlined above will impact the quality of the PSF photometry in a crowded stellar region.

About this Notebook

Author: Matteo Correnti, JWST/NIRCam STScI Scientist II \ Updated on: 2021-01-15